A New Contour-Based Approach to Object Recognition for Assembly Line Robots

In this paper, a fast watershed-based image segmentation algorithm is proposed. Firstly, a homogeneity gradient image named after H-image is produced, and then the traditional watershed segmentation is used to initially segment the H-image to produce over-segmented image regions (objects), and at last local optimum merging strategy is utilized to merge neighbor regions to construct a hierarchical network of image objects according to different scales. Textural, color and shape information of segments is utilized in the merging process. Experiments demonstrate the validity of the algorithm proposed in this paper.

Acquiring and processing phase-contrast microscopy images in wide-field long-term live-cell imaging and high-throughput screening applications is still a challenge as the methodology and algorithms used must be fast, simple to use and tune, and as minimally intrusive as possible. In this paper, we developed a simple and fast algorithm to compute the cell-covered surface (degree of confluence) in phase-contrast microscopy images. This segmentation algorithm is based on a range filter of a specified size, a minimum range threshold, and a minimum object size threshold. These parameters were adjusted in order to maximize the F-measure function on a calibration set of 200 hand-segmented images, and its performance was compared with other algorithms proposed in the literature. A set of one million images from 37 myoblast cell cultures under different conditions were processed to obtain their cell-covered surface against time. The data were used to fit exponential and logistic models, and the analysis showed a linear relationship between the kinetic parameters and passage number and highlighted the effect of culture medium quality on cell growth kinetics. This algorithm could be used for real-time monitoring of cell cultures and for high-throughput screening experiments upon adequate tuning.

A machine learning method is now considered capable of accurately segmenting images. However, one significant disadvantage of this strategy is that it requires a lengthy training phase and an extensive training dataset. This article uses an image segmentation by histogram thresholding approach that does not require training to overcome this difficulty. This article proposes straightforward and time-optimal algorithms, which are guaranteed by mathematical proofs. Furthermore, we experiment with the proposed algorithms using 100 images from a standard database. The results show that, while their performances are not significantly different, the two proposed methods are roughly 10 and 20 times faster than the most simple and optimal method, Brute Force. They also show that the proposed algorithms can deal with bimodal images and images with various shapes of the image histogram. Because our proposed algorithms are the most efficient and effective. As a result, they can be used for real-time segmentations and as a pre-processing approach for multiple object segmentation.

In today's world, increasing life expectation have made the heart failures of important concern. For clinical diagnosis, parameters for the condition of heart are needed. Accurate and fast image segmentation algorithms are of paramount importance prior to the calculation of these parameters. An automatic method for segmenting the cardiac magnetic resonance (CMR) images is always desired to increase the accuracy. We prefer random walk method due to its noise robustness and unconditional approach over other segmentation algorithms. Performance of the method solely depends on the selection of initial seeds, which uses to be decided manually. But there are some hurdles while applying this method to CMR images, due to their color complexity unlike general images. So the main objective to select the number and placement of seeds automatically. We obtain this with use of cantilever beam (CB) equation in association with a variable threshold technique. The highlight of our method is its ability to succeed with minimum number of initial seeds.

In this paper we present a novel algorithm for very fast segmentation of range images into both planar and curved surface patches. In contrast to other known segmentation methods our approach makes use of high-level features (curve segments) as segmentation primitives instead of individual pixels. This way the amount of data can be significantly reduced and a very fast segmentation algorithm is obtained. The proposed algorithm has been tested on a large number of real range images and demonstrated good results. With an optimized implementation our method has the potential to operate in quasi real-time (a few range images per second).

Background: Photographs provide more information including colour, luminance, texture, and shading cues compared to line drawings. There is evidence that these additional cues facilitate image recognition in individuals with and without neurological deficits. Black-and-white line drawings and colour photographs are commonly used with individuals with aphasia in evaluation and treatment. Eye tracking provides an opportunity to assess the influence of these different image types on object recognition processes without relying on verbal responses.Aims: The purpose of this study was to determine whether differences in object recognition of photographs and line drawings were observed using eye-tracking measures. Specific goals were to compare the influence of (a) image type of the same target image presented as colour photograph and black-and-white line drawing and (b) degree of colour diagnosticity (high-colour diagnostic natural objects and low-colour diagnostic human-made objects) in multiple-choice image displays on language-mediated eye movements in language-normal adults.Methods & Procedures: Eye movements of 19 participants were recorded while they viewed 66 multiple-choice image displays of colour photographs and black-and-white line drawings presented with a verbal stimulus. Target images included high-colour diagnostic natural objects and low-colour diagnostic human-made objects.Outcomes & Results: Participants allocated significantly greater proportions of fixation duration and first-pass gaze duration to the photographs compared to line drawings. No significant differences were observed for colour diagnosticity differences within the colour or the black-and-white line drawing displays.Conclusions: Eye tracking indexed significant differences in fixation duration allocated to target images in displays that only differed with respect to image type and colour, and were otherwise carefully controlled for shape, size, orientation, and content conveyed. Results suggest that language-normal participants’ object recognition was facilitated by colour photographs compared to line drawings, highlighting the clinical relevance and the need for research of image design for clinical use with individuals with neurological impairments.

Single-unit recordings demonstrated that the adult mammalian visual cortex is capable of reorganizing after induced retinal lesions. In humans, whether the adult cortex is capable of reorganizing has only been studied using functional magnetic resonance imaging, with equivocal results. Here, we exploited the phenomenon of visual crowding, a major limitation on object recognition, to show that, in humans with long-standing retinal (macular) lesions that afflict the fovea and thus use their peripheral vision exclusively, the signature properties of crowding are distinctly different from those of the normal periphery. Crowding refers to the inability to recognize objects when the object spacing is smaller than the critical spacing. Critical spacing depends only on the retinal location of the object, scales linearly with its distance from the fovea, and is approximately two times larger in the radial than the tangential direction with respect to the fovea, thus demonstrating the signature radial-tangential anisotropy of the crowding zone. Using retinal imaging combined with behavioral measurements, we mapped out the crowding zone at the precise peripheral retinal locations adopted by individuals with macular lesions as the new visual reference loci. At these loci, the critical spacings are substantially smaller along the radial direction than expected based on the normal periphery, resulting in a lower scaling of critical spacing with the eccentricity of the peripheral locus and a loss in the signature radial-tangential anisotropy of the crowding zone. These results imply a fundamental difference in the substrate of cortical processing in object recognition following long-term adaptation to macular lesions.

Image-based and structure-based matching processes in object recognition

Understanding the neural representation of spatial frequency (SF) in the primate cortex is vital for unraveling visual processing in object recognition. While many studies focus on SF in the primary visual cortex, the characteristics and interaction of SF with category representation remain unclear. To explore SF representation in the inferior temporal (IT) cortex of macaques, we conducted extracellular recordings with complex stimuli systematically filtered by SF. Our findings reveal explicit SF coding at both single-neuron and population levels. Temporal dynamics analysis of SF representation reveals that low SF (LSF) is decoded faster than high SF (HSF), and the SF preference dynamically shifts from LSF to HSF over time. Additionally, the SF representation for each neuron forms a profile that predicts category selectivity at the population level. IT neurons cluster into four groups based on SF preference, each with distinct category coding behaviors. Notably, HSF-preferring neurons show the highest category decoding for faces. Despite the existing connection between SF and category coding, we have identified uncorrelated representations of SF and category. Unlike category coding, SF is more sparsely represented and depends more on individual neurons. These findings dissociate SF and category representations, underscoring SF's pivotal role in object recognition.

In recent years, the computer vision applications in the robotics have been improved to approach human-like visual perception and scene/context understanding. Following this aspiration, in this study, we explored the possibility of better object manipulation performance by connecting the visual recognition of objects to their physical attributes, such as weight and center of gravity (CoG). To develop and test this idea, an object manipulation platform is built comprising a robotic arm, a depth camera fixed at the top center of the workspace, embedded encoders in the robotic arm mechanism, and microcontrollers for position and force control. Since both the visual recognition and force estimation algorithms use deep learning principles, the test set-up was named as Deep-Table. The objects in the manipulation tests are selected from everyday life and are common to be seen on modern office desktops. The visual object localization and recognition processes are performed from two distinct branches by deep convolutional neural network architectures. We present five of the possible cases, having different levels of information availability on the object weight and CoG in the experiments. The results confirm that using our algorithm, the robotic arm can move different types of objects successfully varying from several grams (empty bottle) to around 250 g (ceramic cup) without failure or tipping. The proposed method also shows that connecting the object recognition with load estimation and contact point further improves the performance characterized by a smoother motion.

In this paper, we address object recognition for a mobile robot which is deployed in a multistory building. To move to another floor, a mobile robot should recognize various objects related to an elevator, e.g., elevator control, call buttons, and LED displays. To this end, we propose a neural network based retrainable framework for object recognition, which consists of four components--preprocessing, binary classification, object identification, and outlier rejection. The binary classifier, a key component of our system, is a neural network that can be retrained, the motivation of which is to adapt to varying environments, especially with illuminations. Without incurring any extra process to prepare new training samples for retraining, they are freely obtained as a result of the outlier rejection component, being extracted on-line. To realize a practical system, we adopt a parallel architecture integrating both recognition and retraining processes for seamless object recognition, and furthermore detect and cope with the deterioration of a retrained neural network to ensure high reliability. We demonstrate the positive effect of retraining on the object recognition performance by conducting experiments over hundreds of images obtained in daytime and nighttime.

This paper describes motivation and hardware architecture for resizing input image frames from the camera in order to support real-time scale-invariant object recognition. Conventional implementation of object detection algorithms such as histogram of oriented gradients (HOG) based feature extraction, face detection using Haar classifiers often perform image resizing during the object recognition process. Our investigation reveals that this incurs significant performance overhead due to frequent memory accesses that are required for image resizing. This has motivated our approach to perform online resizing - simultaneously resizing of the input image when it is loaded into frame buffer memory - prior to the object recognition process. We propose a hardware architecture to accelerate image resizing and describe a hybrid processor-accelerator platform to generate different sizes of an image in real-time for object recognition.

It is typically assumed that perception for action and object recognition are subserved by functionally and neuroanatomically distinct processing streams in the brain. However, recent evidence challenges this classical view and suggests an interaction between both visual processing streams. While previous studies showed an influence of object perception on action-related tasks, we investigated whether action representations facilitate visual object recognition. In order to address this question, two briefly displayed masked objects were sequentially presented, either affording congruent or incongruent motor interactions. We found superior naming accuracy for object pairs with congruent as compared to incongruent motor interactions (Experiment 1). This action priming effect indicates that action representations can facilitate object recognition. We further investigated the nature of the representations underlying this action priming effect. The effect was absent when the prime stimulus was presented as a word (Experiment 2). Thus, the action priming effect seems to rely on action representations specified by visual object information. Our findings suggest that processes of object-directed action influence object recognition.

The software-assisted planning of radiofrequency-ablation of liver tumors calls for robust and fast methods to segment the tumor and surrounding vascular structures from clinical data to allow a numerical estimation, whether a complete thermal destruction of the tumor is feasible taking the cooling effect of the vessels into account. As the clinical workflow in radiofrequency-ablation does not allow for time consuming planning procedures, the implementation of robust and fast segmentation algorithms is critical in building a streamlined software application tailored to the clinical needs. To suppress typical artifacts in clinical CT or MRT data - like inhomogeneous background density due to the imaging procedure - a Bayesian background compensation is developed, which subsequently allows a robust segmentation of the vessels by fast threshold based algorithms. The presented Bayesian background compensation has proven to handle a wide range of image perturbances in MRT and CT data and leads to a fast and reliable identification of vascular structures in clinical data.

In this paper, we propose a new object recognition framework that combines Gabor energy filters, a visual cortex model in which single units alternate with complex units, and color information. Each color image is first converted to the CIELAB color space. Rather using Gabor filters in the first layer of the cortex model, to each color component, a set of Gabor energy filters is applied. Thereafter, the superposition responses of the Gabor energy filter outputs over the color components are normalized by divisive normalization. In the fourth layer, sparse features are calculated using a localized pooling method that allows retention of some geometric information from the prototype patches’ positions. Finally, a set of sparse features are exploited by a linear SVM classifier for object recognition and classification. In the learning stage, a set of prototypes is selected randomly over spatial position, spatial size, and several scales simultaneously, and is extracted by the local maximum over scales and orientations, ignoring weaker training scales and orientations. The results of experiments performed on several datasets show that the use of color information in our framework improves object recognition significantly.