WormTracer: A precise method for worm posture analysis using temporal continuity.

Color is a very important cue for object recognition, which can help increase the discriminative power of an object-recognition system and also make it more robust to variations in the lighting and imaging conditions. Nonetheless, even though most image acquisition devices provide color data, a lot of object-recognition systems rely solely on simple grayscale information. Part of the reason for this is that although color has advantages, it also introduces some complexities. In particular, the RGB values of a digital color image are only indirectly related to the surface “color” of an object, which depends not only on the object’s surface reflectance but also on such factors as the spectrum of the incident illumination, surface gloss, and the viewing angle. As a result, there has been a great deal of research into color invariants that encode color information but at the same time are insensitive to these other factors. This chapter describes these color invariants, their derivation, and their application to color-based object recognition in detail. Recognizing objects using a simple global image matching strategy is generally not very effective since usually an image will contain multiple objects, involve occlusions, or be captured from a different viewpoint or under different lighting conditions than the model image. As a result, most object-recognition systems describe the image content in terms of a set of local descriptors—SIFT, for example—that describe the regions around a set of detected keypoints. This chapter includes a discussion of the three color-related choices that need to be made when designing an object-recognition system for a particular application: Color-invariance, keypoint detection, and local description. Different object-recognition situations call for different classes of color invariants depending on the particular surface reflectance and lighting conditions that will be encountered. The choice of color invariants is important because there is a trade-off between invariance and discriminative power. All unnecessary invariance is likely to decrease the discriminative power of the system. Consequently, one part of this chapter describes the assumptions underlying the various color invariants, the invariants themselves, and their invariance properties. Then with these color invariants in hand, we turn to the ways in which they can be exploited to find more salient keypoints and to provide richer local region descriptors. Generally but not universally, color has been shown to improve the recognition rate of most object-recognition systems. One reason color improves the performance is that including it in keypoint detection increases the likelihood that the region surrounding the keypoint will contain useful information, so descriptors built around these keypoints tend to be more discriminative. Another reason is that color-invariant-based keypoint detection is more robust to variations in the illumination than grayscale-based keypoint detection. Yet another reason is that local region descriptors based on color invariants more richly characterize the regions, and are more stable relative to the imaging conditions, than their grayscale counterparts.

The underwater visibility is very important in underwater vision research and target detection. However, most underwater vision systems cannot guarantee to possess the performance under complex water conditions. This is because underwater images are usually degraded by light-water interactions of absorption and scattering. The ambient light is scattered into the cameras line of sight by water molecules and suspended particles in the water medium, which adds a layer of haze to the image and reduces the contrast of the image. This part of scattered light is usually called background light, which is the main reason for underwater image degradation. In this paper, the formations of background light in underwater imaging under two different lighting conditions: natural illumination and artificial lighting, are analyzed by setting up physical models. The models developed include the parameters such as camera parameters, light source parameters, inherent optical properties, and camera-source-object geometry. Based on the models, the relationship between the background light and the above parameters is studied. Computer analysis shows that the global background light under two illumination conditions has a close relationship between the inherent optical properties of water medium and camera parameters. The global background light under natural illumination is proportional to the scattering coefficient and inversely proportional to the attenuation coefficient. The background light under the two illumination conditions both can be described in simple exponential falloff expressions of the global background light. The simple expression greatly reduces the computational complexity of simulations. The intensity of background light mainly depends on the inherent optical properties, camera-scene distance, camera-source distance and cameras imaging angle. The relationship between the global background light and the inherent optical properties can be used to estimate the attenuation coefficient, scattering coefficient and scene depth information. The result of this paper can be very useful for designing and improving the underwater imaging systems.

High-level structure (HLS) extraction recovers 3D elements on human-made surfaces (objects, buildings, ground, etc.). There are several approaches to HLS extraction. However, most of these approaches are based on processing two or more images captured from different camera views or on processing 3D data in the form of point clouds extracted from the camera images. In general, 3D point cloud and multiple views approaches have good performance for certain scenes with video sequences or image sequences, but they need sufficient parallax in order to guarantee accuracy. To address this problem, an alternative is to process a single RGB image seeking to interpret areas of the images where the human-made structure may be observed, thus removing parallax dependency, but adding the challenge of having to interpret image ambiguities correctly. Motivated by the latter, we propose a methodology for 3D volumetric structure extraction from a single image. Our strategy is to divide and simplify the 3D structure extraction process. For that, our methodology has three steps. First, the structure recognition step provides the segmentation, location, and delimitation of the urbanized structures in the scene. Second, we propose a graph analysis to classify and locate the boundaries between the different urbanized structures in the scene. Third, we use a proposed CNN and the pinhole camera model to extract the 3D volumetric structure. On the other hand, we evaluate this methodology in synthetic and public datasets.

The effect of different brightness conditions on visually and memory guided saccades

Chapters 5 and 6 have focused quite specifically on key intersemiotic relations between words and images in print news stories that typically draw on a single photographic image in the retelling of an event. In this chapter, I will focus on intrasemiotic relations in other forms of news discourse where sequences of multiple images are used to tell a story. Quite specifically, I will draw on instances where sequences of still images are used together in online news galleries and examine the extent to which cohesive visual narratives are formed within that sequence of images.1

Voluntary regulation of lighting by domestic fowls in Skinner boxes

Silent Speech Interfaces aim to reconstruct the acoustic signal from a sequence of ultrasound tongue images that records the articulatory movement. The extraction of information about the tongue movement requires us to efficiently process the whole sequence of images, not just as a single image. Several approaches have been suggested to process such a sequential image data. The classic neural network structure combines two-dimensional convolutional (2D-CNN) layers that process the images separately with recurrent layers (e.g. an LSTM) on top of them to fuse the information along time. More recently, it was shown that one may also apply a 3D-CNN network that can extract information along both the spatial and the temporal axes in parallel, achieving a similar accuracy while being less time consuming. A third option is to apply the less well-known ConvLSTM layer type, which combines the advantages of LSTM and CNN layers by replacing matrix multiplication with the convolution operation. In this paper, we experimentally compared various combinations of the above mentions layer types for a silent speech interface task, and we obtained the best result with a hybrid model that consists of a combination of 3D-CNN and ConvLSTM layers. This hybrid network is slightly faster, smaller and more accurate than our previous 3D-CNN model.KeywordsSilent speech interfaceConvolutional neural network3D convolutionConvLSTMUltrasound tongue video

Objectives: Cardiac Image Segmentation field have a lot of difficulties when we take the big changes in sequences of images that are of different types. In image sequences, Segmentation of objects which are not fixed is more challenging. To handle such situations, use of Improved Genetic Algorithm for Image Segmentation of Cardiac images is presented.Methods: We propose an algorithm based on Improved Genetic Algorithm, for segmentation of medical image sequences, which uses K-mean clustering. For clustering in the feature space, we used feature vector of two- dimension. Findings: In our paper, for Cardiac Image Segmentation, we are presenting a state of art review of various methods and techniques. Various sequences of Cardiac image have been Registered, and then for segmentation process, single image is used. Novelty/Improvement: Satisfactory results have been given by the experiments done on Cardiac images.

This paper describes the preliminary stages in the development of a predictive feed-forward (PFF) stereo based tracking module. The object of the module is to exploit the spatio-temporal coherence that exists in a sequence of stereo images in the context of providing a visual control mechanism for a mobile vehicle with uncertainty in position. PFF provides a method by which the representation of a 3D scene can be maintained and evolved over time. Furthermore, quickening strategies can utilise the spatio-temporal coherence by exploiting previously obtained depth values and approximate trajectory information in order to accelerate the process that actually achieves the stereo correspondences. Much research in computer vision has been developed in snapshot mode, concentrating attention on a single image or a small number of frames obtained either in synchrony or from a short movie sequence, the computational overheads involved in the analysis of a long sequence of images having proved prohibitive. It has been assumed, albeit implicitly, that algorithms developed in this way would, given appropriate parallel computer architectures, eventually be able to perform in real time on continuous image sequences. That is, the whole process involved in the recovery of scene descriptions would begin afresh upon each image frame and that some [not fully defined] extra module would be responsible for maintaining an evolving model of the environment based upon these descriptions. In the context of the control of a mobile robotic vehicle it is important to distinguish between the twin goals of obtaining an accurate model of the environment and determining the current position within it. Hence dynamic vision can be decomposed into [at least] two important modules: 1) The maintenance of an accurate and, as far as possible, topologically complete scene model. This will include: the combination of multiple views [1,2] to give more complete and robust data; the identification of and inclusion of novel (not previously seen) features; the determination of free space within which the robot can move. 2) The use of visual tracking to provide the control signal required to navigate an autonomous vehicle through an unstructured/partially structured environment using as beacons a subset of the scene features (perhaps identified outside the tracking module itself). The visual through-put and temporal response required by each task is very different. For example, when using visual feed back as a control mechanism it will be necessary to provide a much higher sample rate than that at which the model of the environment needs to be updated. Furthermore, the actual elapsed time

The quality and information content of biological images can be significantly enhanced by postacquisition processing using deconvolution and denoising. However, when imaging complex biological samples, such as neurons, stained with fluorescence labels, the signal level of different structures can differ by several orders of magnitude. This poses a challenge as current image reconstruction algorithms are focused on recovering low signals and generally have sample-dependent performance, requiring tedious manual tuning. This is one of the main hurdles for their wide adoption by nonspecialists. In this work, we modify the general constrained reconstruction method (in our case utilizing a total variation constraint) so that both bright and dim structures can drive the deconvolution with equal force. In this way, we can simultaneously obtain high-quality reconstruction across a wide range of signals within a single image or image sequence. The algorithm is tested on both simulated and experimental data. When compared with current state-of-art algorithms, our algorithm outperforms others in terms of maintaining the resolution in the high-signal areas and reducing artefacts in the low-signal areas. The algorithm was also tested on image sequences where one set of parameters are used to reconstruct all images, with blind evaluation by a group of biologists demonstrating a marked preference for the images produced by our method. This means that our method is suitable for batch processing of image sequences obtained from either spatial or temporalscanning. LAY DESCRIPTION: Fluorescence microscopy images of complex biological samples contain a wide range of signal levels. This signal variation leads current reconstruction algorithms, which aim to enhance the quality of the raw images, to have sample-dependent performance. In this work, we design a new optimization that allows the reconstruction to "pay equal eqattention to" both bright and dim structures. In this way, we can simultaneously recover both bright and dim structures within a single image or image sequence, as validated when the algorithm was quantitatively tested on both simulated and experimental data. When our method was evaluated alongside current state of art algorithms by a group of biologists, our algorithm was considered qualitativelysuperior.

When driving on the road many things can draw attention of the main task for driving, for example advertisement. The success of an advertising depends on how well it was able to communicate with the target properly. Information processing consisting of the perception process, organizing, and remembering of receiving information. This study tries to assess four-wheel drive behaviour when he saw advertisement on the road using The Eye Tracking method to know Allocation Time of Visual Spare Capacity, Attention Level and Understanding Level driver’s. From 20 people who are respondents only 15 respondents have seen advertisement displayed and obtained research results that while driving, drivers have allocation of time of 6,52 second or 3,78 % of the whole long drive duration. With a small percentage, the attention of driver had low results seen from the perspective of eye integration for the advertisement. In this research also known that images and colour are easier to remember compared with the number of pictures displayed. So, the cognitive recommendation that can be given are the minimum distance between advertisement of 890 meters, Maximum element in advertisement contain 8 elements, Using the dark background in light coloured or otherwise, avoid several colours that hard to read if put together and Using Uppercase or Lowercase Font Style.

In materials science, high-throughput material processing and testing are crucial for rapid materials design and discovery, but manual operations often create bottlenecks in terms of speed and accuracy. Automating the repetitive and labor-intensive aspects of material testing significantly increases throughput and consistency, facilitating a more efficient pathway to material innovation. This study demonstrates automated process control by integrating robotic automation in a high-throughput laser shock system using closed-loop “see-move-shoot” experiments. The system employs two automated linear stages to sequentially manipulate material specimens (flyers) under a laser for impact testing. Each experiment includes automatic target detection, sample centering, laser activation, impact verification, and progression to the next sample. For flyer detection, we developed a computer vision algorithm using a convolutional neural network (CNN) model based on EfficientNetB0. Trained on 16,000 labeled images under various lighting conditions, the model achieved a root mean square error (RMSE) of 0.038 mm in extreme testing conditions (i.e., under high exposure, low light, or blurry images) ensuring reliable and efficient real-time processing. In our “see-move-shoot” comparison study, manual operation took 30.6 seconds for a novice user and 19.2 seconds for an expert user per shot, while the CNN model required only 7.46 seconds. Consequently, the CNN model conducts experiments 4 times faster than the novice user and 2.5 times faster than the expert user.

A new species of Scobinancistrus from the Xingu River, Brazil, is described. It can be distinguished from its congeners by color pattern and a combination of non-exclusive characters: overall body covered by large yellow spaced blotches over a dark background (vs. small round and densely packed spots over light or dark background in S. pariolispos and S. aureatus); lack of orange to yellow/orange distal band on dorsal and caudal fins (vs. presence in S. aureatus), dorsal fin not reaching adipose-fin supporting plate when adpressed (vs. reaching the adipose-fin plate in S. pariolispos and S. aureatus). The new species is only known from a portion of the middle Xingu River, ranging from the Volta Grande do Xingu, an area under a strong anthropic impact due to the construction of the Belo Monte dam, to near the Iriri River confluence with the Xingu River. Aspects concerning the species’ threats and its conservation status are discussed.

The remote, inaccessible location of many rivers in Alaska creates a compelling need for remote sensing approaches to streamflow monitoring. Motivated by this objective, we evaluated the potential to infer flow velocities from optical image sequences acquired from a helicopter deployed above two large, sediment-laden rivers. Rather than artificial seeding, we used an ensemble correlation particle image velocimetry (PIV) algorithm to track the movement of boil vortices that upwell suspended sediment and produce a visible contrast at the water surface. This study introduced a general, modular workflow for image preparation (stabilization and geo-referencing), preprocessing (filtering and contrast enhancement), analysis (PIV), and postprocessing (scaling PIV output and assessing accuracy via comparison to field measurements). Applying this method to images acquired with a digital mapping camera and an inexpensive video camera highlighted the importance of image enhancement and the need to resample the data to an appropriate, coarser pixel size and a lower frame rate. We also developed a Parameter Optimization for PIV (POP) framework to guide selection of the interrogation area (IA) and frame rate for a particular application. POP results indicated that the performance of the PIV algorithm was highly robust and that relatively large IAs (64–320 pixels) and modest frame rates (0.5–2 Hz) yielded strong agreement ( R 2 > 0.9 ) between remotely sensed velocities and field measurements. Similarly, analysis of the sensitivity of PIV accuracy to image sequence duration showed that dwell times as short as 16 s would be sufficient at a frame rate of 1 Hz and could be cut in half if the frame rate were doubled. The results of this investigation indicate that helicopter-based remote sensing of velocities in sediment-laden rivers could contribute to noncontact streamgaging programs and enable reach-scale mapping of flow fields.

Long-range and high-pixel-resolution LiDAR systems, using Time-of-Flight (ToF) information of the reflected photon from the target, are essential upon launching safe and reliable self-driving programs of Level 4 and above. 200m long-range distance measurement (DM) is required to sense proceeding vehicles and obstacles as fast as possible in a highway situation. To realize safe and reliable self-driving in city areas, LiDAR systems uniting wide angle-of-view and high pixel resolution are required to fully perceive surrounding events. Moreover, these performances must be achieved under strong background light (e.g., sunlight), which is the most significant noise source for LiDAR systems. To accomplish a 100m-range DM, an accumulation of the DM results through several pixels is utilized to improve the S/N ratio with 70klux background light [1]. Here, S is the number of photons reflected from the target and N as the number of background light photons. However, if the range is extended to 200m under similar condition of the laser power and frame rate (FPS), 16x more pixel accumulation is required. Such pixel accumulation leads to blurring the range image, and hence, a serious oversight in the surrounding events, such as a flying-out pedestrian, may occur, not suiting self-driving applications. Furthermore, the Time-to-Digital Converter (tDC) based ToF measurement is activated only when 2 or more photons are detected simultaneously [1], and thus, is not suitable for the 200m long-range DM where few photons are reflected from the target. On the other hand, ToF measurements using ADCs, which can continuously quantize the silicon photomultiplier (SiPM) output and can sense single-photon events, suits long-range measuring purposes well [2]. However, a number of accumulations should still be required to accomplish 200m-range DM, and hence, low resolution is inevitable. In addition, the SoC cost is critical. To enhance the short-range DM resolution by using ADCs, the required sampling rate is over 10GS/s; upon realizing a 20ch AFE, such an ADC array alone may occupy an area of over 10mm2 and consume huge power [3].