Interactive Object Recognition through Hypothesis Generation and Confirmation

Identification of objects in a real world plays a key role for human-computer interaction in a computer-augmented environment using augmented reality techniques. For providing natural interaction in such environments, it is necessary for an interface system to know which objects a user is using. In the previously developed interface systems, real objects are identified by using specially designed tags attached to objects. In this work, we propose a new method for interactive object recognition and registration for more natural and intuitive interaction without using any tags. In particular, we introduce interactive object registration and recognition by combining direct manipulation with user's hands and a color-based object recognition algorithm.

A robot vision system can be called humanoid if it possesses an oculomotor system similar to human eyes and if it is capable to simultaneously acquire and process images of varying resolution. Designers of a number of humanoid robots attempted to mimic the foveated structure of the human eye. Foveation is useful because, firstly, it enables the robot to monitor and explore its surroundings in images of low resolution, thereby increasing the efficiency of the search process, and secondly, it makes it possible to simultaneously extract additional information – once the area of interest is determined – from higher resolution foveal images that contain more detail. There are several visual tasks that can benefit from foveated vision. One of the most prominent among them is object recognition. General object recognition on a humanoid robot is difficult because it requires the robot to detect objects in dynamic environments and to control the eye gaze to get the objects into the fovea and to keep them there. Once these tasks are accomplished, the robot can determine the identity of the object by processing foveal views. Approaches proposed to mimic the foveated structure of biological vision systems include the use of two cameras per eye (Atkeson et al., 2000; Breazeal et al., 2001; Kozima & Yano, 2001; Scassellati, 1998) (Cog, DB, Infanoid, Kismet, respectively), i. e. a narrow-angle foveal camera and a wide-angle camera for peripheral vision; lenses with space-variant resolution (Rougeaux & Kuniyoshi, 1998) (humanoid head ESCHeR), i. e. a very high definition area in the fovea and a coarse resolution in the periphery; and space-variant log-polar sensors with retina-like distribution of photo-receptors (Sandini &Metta, 2003) (Babybot). It is also possible to implement log-polar sensors by transforming standard images into log-polar ones (Engel et al., 1994), but this approach requires the use of high definition cameras to get the benefit of varying resolution. Systems with zoom lenses have some of the advantages of foveated vision, but cannot simultaneously acquire wide angle and high resolution images. Our work follows the first approach (see Fig. 1) and explores the advantage of foveated vision for object recognition over standard approaches, which use equal resolution across the visual field. While log-polar sensors are a closer match to biology, we note that using two cameras per eye can be advantageous because cameras with standard chips can be utilized. This makes it possible to equip a humanoid robot with miniature cameras (lipstick size and 1

Image segmentation is one of the most important processes involved in image processing. Segmenting an image is breaking it up into smaller pieces, or segments. These are the areas where it is most helpful because processing the complete image would be inefficient for tasks like object recognition or image compression. Image segmentation is the process of dividing an image's elements for further processing. This article looked at a number of image segmentation techniques, including the threshold approach, edge-based method, and clustering-based method. The segmentation method that is most effective for separating images in OCT images is clustering-based segmentation. To reduce the speckle noise during preprocessing, the wiener filter approach is used. With regards to the threshold, edge-based (Sobel, Canny, Robert's), and clustering-based segmentation techniques, calculate MSE (mean square error) and PSNR (peak signal to noise ratio) values for the segmented image quality. Keywords: Clustering; Edge detection; Image segmentation; Region-based; Threshold.

Introduction: Communication with the hearing impaired ( deaf/mute) people is a great challenge in our society today; this can be attributed to the fact that their means of communication (Sign Language or hand gestures at a local level) requires an interpreter at every instance. Conversion of images to text as well as speech can be of great benefit to the non-hearing impaired and hearing impaired people (the deaf/mute) from circadian interaction with images. To effectively achieve this, a sign language (ASL – American Sign Language) image to text as well as speech conversion was aimed at in this research. Methodology: The techniques of image segmentation and feature detection played a crucial role in implementing this system. We formulate the interaction between image segmentation and object recognition in the framework of FAST and SURF algorithms. The system goes through various phases such as data capturing using KINECT sensor, image segmentation, feature detection and extraction from ROI, supervised and unsupervised classification of images with K-Nearest Neighbour (KNN)-algorithms and text-to-speech (TTS) conversion. The combination FAST and SURF with a KNN of 10 also showed that unsupervised learning classification could determine the best matched feature from the existing database. In turn, the best match was converted to text as well as speech. Result: The introduced system achieved a 78% accuracy of unsupervised feature learning. Conclusion: The success of this work can be attributed to the effective classification that has improved the unsupervised feature learning of different images. The pre-determination of the ROI of each image using SURF and FAST, has demonstrated the ability of the proposed algorithm to limit image modelling to relevant region within the image.

We are developing a helper robot that carries out tasks ordered by the user through speech. The robot needs a vision system to recognize the objects appearing in the orders. It is, however, difficult to realize vision systems that can work in various conditions. Thus, we have proposed to use the human user's assistance through speech. When the vision system cannot achieve a task, the robot makes a speech to the user so that the natural response by the user can give helpful information for its vision system. Our previous system assumes that it can segment images without failure. However, if there are occluded objects and/or objects composed of multicolor parts, segmentation failures cannot be avoided. This paper presents an extended system that tries to recover from segmentation failures using photometric invariance. If the system is not sure about segmentation results, the system asks the user by appropriate expressions depending on the invariant values. Experimental results show the usefulness of the system.

Object recognition is a typical task of aerial reconnaissance and especially in military applications, to determine the class of an unknown object on the battlefield can give valuable information on its capabilities and its threat. RecceMan (Reconnaissance Manual) is a decision support system for object recognition developed by the Fraunhofer IOSB. It supports object recognition by automating the tedious task of matching the object features with the set of possible object classes, while leaving the assessment of features to the trained human interpreter. The quality of the features assessed by the user is influenced by several factors such as the quality of the image of the object. These factors are potential sources of error, which can lead to an incorrect classification and therefore have to be considered by the system. To address this issue, two methods for consideration of uncertainty in human feature assessment - a probabilistic and a heuristic approach - are presented and compared based on an experiment in the exemplary domain of flower recognition.

In day-to-day life, new technologies are emerging in the field of Image processing, especially in the domain of segmentation. Image segmentation is the most important part in digital image processing. Segmentation is nothing but a portion of any image and object. In image segmentation, the digital image is divided into multiple set of pixels. Image segmentation is generally required to cut out region of interest (ROI) from an image. Currently there are many different algorithms available for image segmentation. This chapter presents a brief outline of some of the most common segmentation techniques (e.g. Segmentation based on thresholding, Model based segmentation, Segmentation based on edge detection, Segmentation based on clustering, etc.,) mentioning its advantages as well as the drawbacks. The Matlab simulated results of different available image segmentation techniques are also given for better understanding of image segmentation. Simply, different image segmentation algorithms with their prospects are reviewed in this chapter to reduce the time of literature survey of the future researchers.

Image recognition and image processing usually contain the technique of image segmentation. Excellent segmentation results can directly affect the accuracy of image recognition and processing. The essence of image segmentation is to segment each frame of a certain image or a video into multiple specific objects or regions and represent them with different labels. This paper focuses on the segmentation results obtained in image segmentation of images used for intelligent monitoring of Mandarin exams are usually visualized for image analysis. In this paper, we first investigate the performance improvement techniques for semantic segmentation in the image segmentation task for intelligent monitoring of Mandarin exams, improve the pixel classification capability by performing semantic migration, and, for the first time, extend the dataset substantially by style transformation to improve the model’s recognition of advanced features. In addition, to further address the shortcomings of the dataset, this paper improves the performance of image segmentation using synthetic datasets by investigating synthetic dataset image segmentation improvement techniques that reduce the reliance on manually annotated datasets. Image segmentation techniques continue to advance, and there are even thousands of commonly used segmentation methods for image segmentation development to date. Among them, they can be broadly classified as region-based segmentation methods, threshold-based segmentation methods, edge-based segmentation methods, specific theory-based segmentation methods, and deep learning-based segmentation methods. However, the methods used in this paper have all been experimentally demonstrated to improve the effectiveness of the techniques and proved to outperform other existing methods in the same field in the publicly available datasets LSUN, Cityscapes, and GTA5 datasets, respectively.

Clustering-based segmentation algorithm is one of the common methods in image segmentation. However, how to avoid the influence of the initialization and the singular value is a problem we have to face. For overcoming this problem, we proposed a new image segmentation technology which is based on the AIC criterion and not affected by the singular values. The simulation results show that the method has good performance on stability and accuracy. Image segmentation is the key step in image analysis of computer vision and image processing field. It has been widely applied in various areas such as in the process of automating production, sensing images and biomedical images. The main goal of image segmentation is to simplify an image into segments that have a strong correlation with objects in the real world. The existing segmentation algorithm can be generally divided into three classes: edge detection, region- based segmentation and data clustering. Edge detection method relies on the edges of the object with background and the other objects. For the images with noise, it is difficult to separate the boundary and the target background (1). Region- based segmentation method segments an image into several meaningful sub-domains that are non-overlapping and same- nature. But it may lead to the over-segmentation, and has to combine with other methods to use (2). Data clustering method is based on the whole image and considers the distance between each data. Advantages of data clustering method are low complexity and easy to implement. Disadvantages are sensitive to the noise and selection of the initial centroids (3). Data clustering is one of the common techniques in image segmentation. Its purpose is to cluster pixels into several parts (regions) according to the feature of image. Based on the properties of clustering algorithm, researchers have proposed various image segmentation algorithm, such as based on hierarchical clustering, based on K-means clustering, based on fuzzy clustering, based on mean shift, and so on (3-5). Clustering analysis is an unsupervised process of partitioning a data set into subsets of similar data objects. The elementary principle is as far as possible to increase the difference between categories and reduce the difference within the category. In the process of image segmentation based on data clustering, thus the objective is that the similar pixels are divided into the same regions and the non-similar pixels are divided into different regions. However, Two difficulties we must face are that how to determine the cluster centers and avoid influence of noise. In this paper, a new image segmentation algorithm is proposed based on the AIC criterion. The new segmentation technology can achieve an ideal performance and not affected by the initialization of centroids and the singular values. The simulation results verify the efficiency of proposed algorithm.

Image Segmentation is a technique that partitioned the input image into prerequisite semantic unique regions. Segmentation should stop as object of interest in an application is isolated. The ultimate goal is to make the image more simplified one and that to get more meaningful to analyze. Number of segmentation techniques are available but none of them satisfy the global properties and thus remain challenge for researcher. Many computer applications like Object Recognition, Automatic pictorial Pattern Recognition, Automatic Traffic control are based on this analysis. As per need of an application segmentation techniques can be selected . This survey addressed various segmentation techniques, discussed fundamental methodologies , and issues related with specific techniques. It discussed its limitations and probable solution to recover it. It also include discussion on segmentation technique based on graph partitioning which would be helpful to add intelligence for prediction. Concept of Ontology is introduced in short as technical bridge in between segmentation and image prediction.

In this ascension era of technology, Magnetic resonance imaging (MRI) emerges as the utmost clinically acceptable imaging modality for detection and diagnosis of tumors. The Breast tumor is leading scrupulous diseases among women. In last two decades, image segmentation has got a high boost and attention from the researchers across the globe. To represent the image in such a way which is easy to analyze and more meaningful, the process of segmentation is used. It is the primal step in processing images of different types. Therefore, the image is sectioned into desirable building blocks. Basically, it provides the meaningful objects of the image. Literature provides a variety of image segmentation algorithms even though there is a requirement of an efficient segmentation technique which can work efficiently on all sorts of images. The key extract of an algorithm lies within the superiority of segmentation performed by a particular method. The availability of segmentation algorithms is quite large, so the analysis of these algorithms might be interesting to the researchers. This paper reviews segmentation techniques such as theory-based, region-based, thresholding, edge-based, Neural Network-based, Model-based, and Partial differential equation based on the basis of their functioning, utility, advantages, disadvantages, and applications.

Finding efficient and powerful techniques for representing 2D and 3D entities is central in many areas, such as computer vision, graphics, CAD or data visualization. The large amount of data often involved and the small processing time required place strong demands on modeling techniques. This chapter gives an overview of the possibilities wavelets offer for object modeling, specially from the point of view of object recognition in computer vision environments. For the 2D case, techniques based on segmented contours or raw grey level information are described. For 3D data, both the cases of dense, regularly sampled volumetric information and sparse, irregularly sampled geometric surface information are considered. Ideas on how Bayesian methods may enhance wavelet based techniques for object representation and recognition are outlined.

Image segmentation is the fundamental step to analyze images and extract data from them. This paper concentrates on the idea behind the basic methods used. Image segmentation is the process of partitioning a digital image into multiple segments(i. e. sets of pixels). The goal of segmentation is to simplify or change the representation of an image into something that is more meaningful and easier to analyze. Segmentation is typically used to locate objects and boundaries(line, curve) in image. The Image segmentation is a set of segments that collectively cover the entire image. Each of pixels in a region are similar with respect to some characteristics colour, intensity or texture. The various segmentation techniques discussed in this paper.

Image segmentation is part of the image processing and it is important as it has been used in various fields such as medical imaging, autonomous driving and object detection. The selection of technique that is suitable for application is crucial in order to get a good result. The problem occurs when one would like to choose the right method that will give a good segmented image. Therefore, this paper presents the image quality assessment for image segmentation algorithms in terms of qualitative and quantitative analyses. In this project, three techniques of image segmentation which are K-means clustering, threshold and watershed marker controlled are compared with each other and the evaluation result is based on the image quality assessment. Those three methods used to segment greyscale images are taken from the Internet. The objective is to find which technique gives the best result based on the image quality assessment. The parameters used to analyse the quality of the processed image are the mean square error (MSE), average difference (AD), mean absolute error (MAE), structural similarity index metric (SSIM), also the structural dissimilarity index metric (DSSIM). The algorithm of the image segmentation techniques is built using open CV-C++. Based on the assessment, the results, it shows that K-means clustering gives a better result qualitatively and quantitatively while the watershed marker-based shows a good qualitative result. This quality assessment technique hopefully could help in the selection of the image segmentation technique for the respective applications.

Hybrid leader corona virus herd optimizer with multilevel thresholding techniques for foreground and background image segmentation