Active/space-variant object recognition

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.

In Image Processing, the problem of Object Recognition is regarded as difficult. It'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% for each label returned. It accomplishes this by using AWS’ 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.

3-D object recognition is a process of matching an object to a scene description to determine the object’s identity and/or its pose (position and orientation) in space. The problem of object recognition can be separated into two closely related subproblems — that of model building and that of recognition. Object recognition using multiple-view approach is achieved by finding a best match between the description of object projection and one of the model descriptions in the database. An object is modeled by its 2-D projections as seen from a set of predetermined viewpoints uniformly distributed on a view sphere enclosing the object. In our experiments, an icosahedron (twenty-sided) is used to approximate the view sphere. The method to search the model database is crucial to the performance of the recognition process. To recognize an object, a coarse-to-fine search strategy is adopted to reduce the processing time. At the coarse search stage, a small set of views most similar to the object in the scene is located. In most cases, this step vastly decreases the search space. The fine (or exhaustive) search process starts with these located views and compares the topology of the object in the scene with the projections rendered by the neighboring viewpoints of the located viewpoints until a best match is found. In this paper, a FKCN model which is an integration of Fuzzy c-Means (FCM) and Kohonen clustering networks (KCNs) is proposed to exploit the parallelism at the coarse search stagein object recognition, where the learning rate for Kohonen updating is proposed to be: $$\alpha _{ik,t}= (u_{ik,t} )^m t$$ where \(\alpha _{ik,t}= \left( {\sum {\left( {\parallel x_k- v_i \parallel _A /\parallel _{x_k }- v_j \parallel _A } \right)^{2/(m - 1)} } } \right)^{ - 1}\) by minimizing \(\sum\limits_i {\sum\limits_{\mathfrak{k}^u ik} m } \left( {\parallel x_k- v_i \parallel _A } \right)^2\),but weighting exponent m is a function of t, the iterate number, i.e. m = mt = (m0+tΔm) (an increasing strategy). Feature vectors are first extracted from each view. Then a FKCN clusters these feature vectors to get cluster centers, each of which is regarded as a prototype of some similar views. Finally, a feature vector from an input view is compared to the cluster centers instead of all views and finds the potential candidates which are clustered into the closest cluster center in order to achieve the goal of reducing search space.

Model-based object recognition has been a very active research field in Computerter Vision during the past ten years. Several excellent survey papers have been published, for example, [10.1,2]. Crimson has published a book [10.3] describing an extended series of experiments, carried out by himself and his colleagues at MIT, on the role of geometry in solving the object recognition and localization problem. Their approach is mainly based on the search in the interpretation tree, as briefly presented in Sect. 6.1. One chapter of the book by Faugeras [10.4] is also devoted to this problem based in detail with this immense subject. We want simply to adapt our motion-determination algorithm developed in Chap. 7 to solve some primitive object recognition problems.

Deletion of the GluA1 AMPA receptor subunit impairs short-term spatial recognition memory. It has been suggested that short-term recognition depends upon memory caused by the recent presentation of a stimulus that is independent of contextual–retrieval processes. The aim of the present set of experiments was to test whether the role of GluA1 extends to nonspatial recognition memory. Wild-type and GluA1 knockout mice were tested on the standard object recognition task and a context-independent recognition task that required recency-dependent memory. In a first set of experiments it was found that GluA1 deletion failed to impair performance on either of the object recognition or recency-dependent tasks. However, GluA1 knockout mice displayed increased levels of exploration of the objects in both the sample and test phases compared to controls. In contrast, when the time that GluA1 knockout mice spent exploring the objects was yoked to control mice during the sample phase, it was found that GluA1 deletion now impaired performance on both the object recognition and the recency-dependent tasks. GluA1 deletion failed to impair performance on a context-dependent recognition task regardless of whether object exposure in knockout mice was yoked to controls or not. These results demonstrate that GluA1 is necessary for nonspatial as well as spatial recognition memory and plays an important role in recency-dependent memory processes.

Comparative perspectives on multiple cortical visual systems

Robust scene recognition using a GA and real-world raw-image

Unsupervised shape discovery using synchronized spectral networks

We consider the problem of object recognition via local geometric feature matching in the presence of sensor uncertainty, occlusion, and clutter. We present a general formulation of the problem and a polynomial-time algorithm which guarantees finding all geometrically feasible interpretations of the data, modulo uncertainty, in terms of the model. This formulation applies naturally to problems involving both 2D and 3D objects.The primary contributions of this work are the presentation of a robust, provably correct, polynomial-time approach to this class of recognition problems and a demonstration of its practical application; and the development of a general framework for understanding the fundamental nature of the geometric feature matching problem. This framework provides insights for analyzing and improving previously proposed recognition approaches, and enables the development of new algorithms.

Small target detection based on automatic ROI extraction and local directional gray&entropy contrast map

This research investigates fundamental problems in object recognition in earthen heritage and addresses the possibility of an automatic crack detection method for rammed earth images. We propose and validate a straightforward support vector machine (SVM)-based bidirectional morphological approach to automatically generate crack and texture line maps through transforming a surface image into an intermediate representation. Rather than relying on the application of the eight connectivity rule to a combination of horizontal and vertical gradient to extract edges, we instruct an edge classifier in the form of a support vector machine from features computed on each direction separately. The model couples a bidirectional local gradient and geometrical characteristics. It constitutes of four elements: (1) bidirectional edge maps; (2) bidirectional equivalent connected component maps; (3) SVM-based classifier and (4) crack and architectural line feature map generation. Relevant details are discussed in each part. Finally, the efficiency of the proposed algorithm is verified in a set of simulations that is satisfactorily conforming to labeled data provided manually for surface images of earthen heritage.

Our visual world contains numerous objects. An important function of the visual system is to recognize an object by comparing perceptual and memory representations. Although we seldom have any problems in recognizing natural objects, which promotes the belief that object recognition is a quite simple process of matching perceptual and memory representations, recognition does in fact involve extremely complicated visual processing. The fact that objects are almost never presented in isolation illustrates the complexity and difficulty of object recognition. The cluttered nature of our visual environment poses an object segmentation problem (including figure/ground segregation problem), which itself is quite difficult. Even if one can successfully segment a set of objects, there is another problem for the visual system to solve: the so-called binding problem. If there are multiple objects, each of which has its own feature values such as shape, color, size, and so on, then how does the visual system properly maintain the correct correspondences of these features? This chapter focuses on this binding problem in both object recognition and visual working memory.

The article deals with the problems of motion detection, object recognition, and scene description using deep learning in the framework of granular computing and Z-numbers. Since deep learning is computationally intensive, whereas granular computing, on the other hand, leads to computation gain, a judicious integration of their merits is made so as to make the learning mechanism computationally efficient. Further, it is shown how the concept of z-numbers can be used to quantify the abstraction of semantic information in interpreting a scene, where subjectivity is of major concern, through recognition of its constituting objects. The system, thus developed, involves recognition of both static objects in the background and moving objects in foreground separately. Rough set theoretic granular computing is adopted where rough lower and upper approximations are used in defining object and background models. During deep learning, instead of scanning the entire image pixel by pixel in the convolution layer, we scan only the representative pixel of each granule. This results in a significant gain in computation time. Arbitrary-shaped and sized granules, as expected, perform better than regular-shaped rectangular granules or fixed-sized granules. The method of tracking is able to deal efficiently with various challenging cases, e.g., tracking partially overlapped objects and suddenly appeared objects. Overall, the granulated system shows a balanced trade-off between speed and accuracy as compared to pixel level learning in tracking and recognition. The concept of using Z-numbers, in providing a granulated linguistic description of a scene, is unique. This gives a more natural interpretation of object recognition in terms of certainty toward scene understanding.

In this work, we address the problem of 3D object recognition with depth sensors such as Kinect or Structure sensor. Compared with traditional approaches based on local descriptors, which depends on local information around the object keypoints, we propose a novel global features based descriptor. Proposed descriptor, which we name as Differential Histogram of Normal Vectors (DHONV), is designed particularly to capture the surface geometric characteristics of the 3D objects represented by depth images. We describe the 3D surface of an object in each depth image using a 1D histogram capturing the distribution of deferential angles of the surface normal vectors. The object recognition experiments on the benchmark RGB-D object dataset and a self-collected dataset show that our proposed descriptor outperforms two others descriptors based on spin-images and histogram of normal vectors with linear-SVM classifier.

The problem of object or scene recognition is often addressed by seeking geometric image properties that are invariant under changes in viewing conditions. An alternative, non-geometric, ratio method was described by Funt and Finlayson (IEEE Trans. Pattern Anal. Mach. Intell. 17,522, 1995) in which histograms of spatial ratios of colour RGB triplets from neighbouring image regions were used to recognise objects under changes in viewpoint and illumination. In this study, ratio indexing was extended from RGB images to hyperspectral images with a variable number of sensor channels distributed over 400-720 nm. Fifty natural scenes were used to generate test and reference images. For each number of sensors, independent random samples were drawn from each test image of a scene under either a daylight of correlated colour temperature of 25000 K or of 4000 K and matched against independent random samples drawn from each reference image of the scenes under a daylight of correlated colour temperature 6500 K. Matching was based on the intersection of multi-dimensional histograms of ratios of sensor signals in these samples Differences between match hit and false-alarm rates provided a measure of recognition performance. Results suggest that for small samples, indexing with five sensor channels has advantages over indexing with three sensor channels for the recognition of natural scenes.