Region-Based Object Recognition: Pruning Multiple Representationsand Hypotheses

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.

Object recognition is of fundamental importance in computer vision. In a few years, pedestrian detection, car detection, and more generally scene recognition will likely be reliable enough to allow fully-automated car navigation, and the human driver will be relegated to the back seat to sip his coffee. In this thesis we are interested in recognizing individual objects and categories. In order to reduce the volume of information one has to process, images are characterized by sets of features. These features, also called interest points, are targeted at image locations with high local information content. Various systems for detecting interest points and for describing the local image appearance near these points, have been proposed in the last two decades. We investigate which combinations from this plethora of detectors and descriptors, are most suited for object recognition tasks. On to the problem of object recognition, we are first interested in recognizing individual objects. In a few years, one can imagine that customers in shops, will take with their cell phone a picture of a product that looks interesting, send it to a remote server with a huge database of individual objects, and get back information about that specific product. We propose a system for individual object recognition, inspired from previous work on coarse-to-fine recognition. All steps of the recognition process are translated into principled probabilistic terms, which allows us to outperform a state-of-the-art commercial system for individual recognition. Regarding categories, faces are probably the category that has received the most attention in computer vision literature. Here we propose a system to recognize images of the same individual in large databases of images. This can be of high interest when looking for images of a given person over the internet. Our method's advantage is that it works on real-world images, as opposed to the face databases from the literature, collected in laboratories with controlled lighting, pose and background conditions. Finally, we are interested in recognition of object categories in general. Using support vector machines for the classification task, we propose a features-based kernel that improves recognition performance on object categories.

Depth learning has been applied in semantic segmentation and object recognition in computer vision. In this paper, we propose a high-efficiency pixel classification convolutional neural network based on encoder-decoder structure. Using depth image to enhance CNNS network make color image and depth information can be detected jointly. The experimental data shows that through the fusion of depth features representing different scales of information, the algorithm architecture can be jointly optimized and obtain more sophisticated image semantic features. Compared with similar methods, image semantic segmentation algorithm has obvious advantages.

Deep neural networks inspired by the connections among biological neurons have been highly effective in the advancement of computer vision. Recent research into recurrent neural networks has taken account of forward as well as recurrent connections in a neural network architecture. In this study, we proposed a model that mirrors the architecture of the human ventral pathway with separate layers representing brain regions connected using long-range recurrent links. CIFAR-10/100 datasets were used to assess the performance of object recognition using the proposed model and the results were compared with those obtained using state-of-the-art methods. We demonstrated that the classification accuracy increased as the number of recurrences increased. Our results suggest that the proposed neuroscience-inspired model can facilitate object recognition in computer vision and may help to elucidate neurological mechanisms in the human brain.

The paper descries a novel method to extract line segments and circular arcs from the image taken by CCD camera for object recognition. Model based matching is one of the widely used techniques of the object recognition in computer vision, and the position and orientation of the objects are decided with referring the extracted line segments and circular arcs. For the precise and stable decisions of the position and orientation, the accurate extractions of characters are required. These characters are. fast and accurately extracted in the proposed method, while its validity is confirmed in some experiments.

This study examines various algorithms for computing the convex hull of a set of n points in a d-dimensional space. Convex hulls are fundamental in computational geometry and are applied in computer graphics, pattern recognition, and computational biology. Such convex hulls can also be useful in symmetry problems. For instance, when points are arranged symmetrically, the convex hull is also likely to be symmetrically shaped, which can be useful for object recognition in computer vision or pattern recognition. The focus is primarily on two-dimensional algorithms, including well-known methods like Gift Wrapping, Graham Scan, Divide and Conquer, QuickHull, TORCH, Kirkpatrick–Sediel, and Chan’s algorithms. These algorithms vary in terms of time complexity and scalability to higher dimensions. This study is extended to three-dimensional convex hull algorithms, such as NAW, randomized insertion, and parallelized versions, such as CudaHull and CudaChain. This study aimed to elucidate the operational principles, step-by-step procedures, and comparative time complexities of each algorithm. The implementation in Python facilitates a detailed comparison of the algorithmic performance through stepwise analysis and graphical outputs. The ultimate goal is to provide insights into the strengths and weaknesses of each algorithm under various scenarios, thereby offering a comprehensive guide for practical implementation.

Hessian matrix is the square matrix of second partial derivatives of a scalar-valued function and is well known for object recognition in computer vision and medical shape analysis. Previous curvature based polyp detection algorithms generate myriad of false positives. Hessian-matrix based method, however, is more sensitive to local shape features, so easily reduce false positives. Calculation of Hessian matrix on 3D CT data and Eigen decomposition of the matrix gives three Eigen values and vectors at each voxel. Using these Eigen values, we can figure out which type of intensity structures (blob, line, and sheet-like) is on the given voxel. We focus on detecting blob-like object automatically. In the inner colonic wall structures, blob-like, line-like, and sheet-like objects represent polyps, folds and wall, respectively. In addition, to improve the performance of the algorithm, Gaussian blurring factor and shape threshold parameters are optimized. Before Hessian matrix calculation, smoothing the given region using Gaussian kernel with small deviation is necessary to enhance local intensity structures. To optimize the parameters and validate this method, we have produced anthropomorphic pig phantoms. Fourteen phantoms with 103 polyps (16 polyps <6mm, 87 >= 6mm) were used. CT scan was performed with 1mm slice thickness. Our detection algorithm found 84 polyps (81.6%) correctly. Average number of false positives is 7.9 at each CT scan. This results show that our algorithm is clinically applicable for polyp detection, because of high sensitivity and relatively low false positive detections.

This chapter presents a method for image matching which is based on the detection and characterisation of curvilinear regions. In a curvilinear region, the ratio between its average width and its total length should be less than a predefined threshold. Besides, left and right borders should be locally parallel, it should exist a geometric similarity around the region axis and the colour along this axis should be homogeneous. That is, they constitute particular image structures and, therefore, the method is restricted to scenes where these particular items are presented. On the contrary, curvilinear regions automatically deform with changing viewpoint as to keep on covering identical physical parts of a scene. For this reason, they can be used as distinguished regions. The shape contour of these curvilinear regions is characterised using the adaptive curvature function. Experimental results show that this shape descriptor is invariant to rotation and translation, and partially invariant to noise and skewing. Scaling invariance is achieved by employing an extra normalisation stage. Thus, this descriptor, plus the region colour and position, can be used to match curvilinear regions detected on the input image with those previously stored in a database. This is the basis of the correspondence algorithm described in this paper: the similarity index between two images is determined by the presence of the same set of curvilinear regions localised in similar positions. There are many directions for further research. One of this is the integration of several types of distinguished regions. As we commented above, the obligatory presence of these regions in the images is the main disadvantage of the proposed system. Besides, further work must be accomplished in the correspondence algorithm to employ the most similar region correspondences as ground control points. These points could be used to generate a fundamental matrix describing the geometric constraints between the two images. Thus, matches that did not agree with the majority solution could be removed.

In Image Processing, the problem of Object Recognition is regarded as difficult. It&#x0027;s an important aspect of PC vision because so many PC vision programmes rely on it for effectiveness. To address this issue, a number of identifying terminology and frameworks have been discussed for quite some time. The purpose of this paper is to give a briefoverview of a range of platforms in the field of computer vision and object recognition. For the most part this paper covers a relatively mature service from AWS(Amazon Web Services), Amazon Rekognition which is easy to integrate into other services like Lambda, providing huge volume of details in its analysis. The paper incorporates different viewpoints that have been utilized by various analysts for object spotting and recognition. Rekognition is able to recognise objects and scenarios within photographs in this article, returning a confidence measure ranging from 0 to 100&#x0025; for each label returned. It accomplishes this by using AWS&#x2019; Python client Boto3 and the detect labels method. This works by supplying an image, a limit on the number of labels to return, and a confidence level for filtering out ambiguous results.

The role of invariant line junctions in object and visual word recognition

The field of computer vision has undergone rapid development. Starting from recognition tasks such as classification, detection, and segmentation, the focus of visual analysis has gradually shifted towards learning spatiotemporal information. This thesis presents research on spatiotemporal learning, with a particular emphasis on motion estimation and visual recognition. First, we address the problem of video object tracking. Previous methods have primarily re-lied on learning improved appearance representations, while the spatiotemporal relationships of individual objects have been underexplored. We propose leveraging optical flow features to achieve higher generalization in semi-supervised video object segmentation, directly incorporating these features into both the target representation and the decoder network. Our experiments and analysis show that enriching feature representations with spatiotemporal information improves segmentation quality and generalization capability. Next, we investigate spatiotemporal learning in 3D for motion estimation, specifically scene flow estimation. Scene flow estimation as an important research topic in 3D computer vision is crucial for applications such as robotics, autonomous driving, embodied navigation, and tracking. We investigate the problem in different perspectives: 1. What is the best formulation for solving the problem and how to learn a better spatiotemporal feature representation? 2. Can we introduce uncertainty estimation to the task, which is of crucial importance for safety-critical downstream tasks? 3. How to scale the estimation to large-scale data, e.g., autonomous scenes, and leverage the temporal information without introducing much computation overheads? To answer these questions, we explore the use of transformers for improved feature representation, diffusion models for uncertainty estimation, and efficient feature learning methods for multi-frame, large-scale autonomous driving scenarios. Finally, we extend our research to joint visual segmentation, tracking, and open-vocabulary recognition in LiDAR sequences, particularly for autonomous scenes. In such environments, precise segmentation, tracking, and recognition of objects are essential for downstream analysis and control. Current human-annotated open-source datasets allow for reasonable tracking of traffic participants such as cars and pedestrians. However, we aim to advance beyond this towards segmenting and tracking any object in LiDAR data. To this end, we propose a pseudo-labeling engine that leverages the 2D visual foundation model SAM v2 and the vision-language model CLIP to automatically label LiDAR streams. We further introduce the SAL-4D model, capable of segmenting, tracking, and recognizing any object in a zero-shot manner. In summary, we explore the learning of spatiotemporal information in both 2D image and 3D point cloud domains. In the image domain, we demonstrate that spatiotemporal information improves video object segmentation quality and generalization. In the 3D point cloud domain, we show that spatiotemporal learning enables more accurate motion estimation and facilitates the first method for zero-shot segmentation, tracking, and open-vocabulary recognition of arbitrary objects.

To perform daily activities is a very difficult task for visually impaired people, since vision plays a central role in almost every activity of ours. With the advancement in computer vision and computing technologies we can afford to develop a system for visually impaired people, which can give audio feedback of surrounding objects and context. In this paper a novel assistive method for visually impaired people using object and color recognition is proposed, which is very useful for their safety, quality life and freedom from the other person all the time. The system is containing two modules (i) object recognition, (ii) color recognition. Object recognition module recognizes objects like door, chair, stairs, mobile phone etc. and generate an audio feedback to the user, we are using convolution neural network prior to recursive neural network which is followed by softmax classifier. Second module of this system is color recognition which tell them about object color in front of the camera, this module is useful in recognizing clothing colors, fruits color, police(from uniform color) etc. We are using HSI color space with observed thresholding for color recognition. System modules and operation can be selected with an on demand push button panel. The algorithm is evaluated on an online dataset as well as on our dataset.

The Recognition and Classification of Objects Based on the Modified Distance Metric

A single-case study is presented of a patient w ith a modality-specific problem in visual object recognition, w hich can be linked to impaired stored descriptions for objects. Performance was poor on tasks requiring naming, semantic decisions, and object decisions to seen objects. Performance on semantic decisions w ith w ords, however, was relatively good, demonstrating that the problem was not due solely to a general semantic impairment. Moreover, in contrast to the impairments in visual object recognition, both face recognition and on-line visual word recognition w ere within normal limits. The data indicate that im pairments of object recognition can be distinguished from impairments of face and visual w ord recognition, even when the problem can be localised within the structural description system for objects. We discuss the im plications of the results for understanding the relations between object, face, and word identification.

What is the current state-of-the-art for image restoration and enhancement applied to degraded images acquired under less than ideal circumstances? Can the application of such algorithms as a pre-processing step improve image interpretability for manual analysis or automatic visual recognition to classify scene content? While there have been important advances in the area of computational photography to restore or enhance the visual quality of an image, the capabilities of such techniques have not always translated in a useful way to visual recognition tasks. Consequently, there is a pressing need for the development of algorithms that are designed for the joint problem of improving visual appearance and recognition, which will be an enabling factor for the deployment of visual recognition tools in many real-world scenarios. To address this, we introduce the UG 2 dataset as a large-scale benchmark composed of video imagery captured under challenging conditions, and two enhancement tasks designed to test algorithmic impact on visual quality and automatic object recognition. Furthermore, we propose a set of metrics to evaluate the joint improvement of such tasks as well as individual algorithmic advances, including a novel psychophysics-based evaluation regime for human assessment and a realistic set of quantitative measures for object recognition performance. We introduce six new algorithms for image restoration or enhancement, which were created as part of the IARPA sponsored UG 2 Challenge workshop held at CVPR 2018. Under the proposed evaluation regime, we present an in-depth analysis of these algorithms and a host of deep learning-based and classic baseline approaches. From the observed results, it is evident that we are in the early days of building a bridge between computational photography and visual recognition, leaving many opportunities for innovation in this area.