Non-rigid Object Recognition Research Articles

NTT Develops Non-rigid Object Recognition Technology that Achieves Recognition of Database Images--Not Only Rigid 3D Objects but also Non-rigid Objects--at Low Cost

NTT Develops Non-rigid Object Recognition Technology that Achieves Recognition of Database Images--Not Only Rigid 3D Objects but also Non-rigid Objects--at Low Cost

Modal function transformation for isometric 3D shape representation

Isometric deformations complicate 3D shape representation and recognition. Therefore, proper modeling is in need. In this paper, we propose a novel modal function transformation framework for shape information abstraction. Key to our approach is to generalize the notion of pairwise shape matching by comparing their modal functions. By choosing intrinsic embedding basis (e.g. eigenfunction of Laplace–Beltrami Operator (LBO)) properly on each shape, we get a compact function space that is suitable for informative inference. To characterize the interior shape structure, a special inner function is devised. Then, the largest spectra of the inner functions are adopted as our modal feature. On this basis, we discuss the properties of the modal feature. For performance evaluation, non-rigid object recognition experiments are carried out on several popular shape matching benchmarks. The final results show that the proposed approach works well with significant accuracy improvement against the LBO based methods. Besides, the performance of our method is comparable with state-of-the-art results.

SWF-SIFT Approach for Infrared Face Recognition

The scale invariant feature transform (SIFT) feature descriptor is invariant to image scale and location, and is robust to affine transformations and changes in illumination, so it is a powerful descriptor used in many applications, such as object recognition, video tracking, and gesture recognition. However, in noisy and non-rigid object recognition applications, especially for infrared human face recognition, SIFT-based algorithms may mismatch many feature points. This paper presents a star-styled window filter-SIFT (SWF-SIFT) scheme to improve the infrared human face recognition performance by filtering out incorrect matches. Performance comparisons between the SIFT and SWF-SIFT algorithms show the advantages of the SWF-SIFT algorithm through tests using a typical infrared human face database.

인간로봇 상호작용을 위한 언어적 인지시스템 기반의 비강체 인지

For HRI (Human-Robot Interaction) in daily life, robots need to recognize non-rigid objects such as clothes and blankets. However, the recognition of non-rigid objects is challenging because of the variation of the shapes according to the places and laying manners. In this paper, the cognition of non-rigid object based on a cognitive system is presented. The characteristics of non-rigid objects are analysed in the view of HRI and referred to design a framework for the cognition of them. We adopt a linguistic cognitive system for describing all of the events happened to robots. When an event related to the non-rigid objects is occurred, the cognitive system describes the event into a sentential form and stores it at a sentential memory, and depicts the objects with a spatial model for being used as references. The cognitive system parses each sentence syntactically and semantically, in which the nouns meaning objects are connected to their models. For answering the questions of humans, sentences are retrieved by searching temporal information in the sentential memory and by spatial reasoning in a schematic imagery. Experiments show the feasibility of the cognitive system for cognizing non-rigid objects in HRI.

Nonrigid Object Recognition against Complex Background Based on Distributions of Local Patterns

Nonrigid Object Recognition against Complex Background Based on Distributions of Local Patterns

Study on man power planning of hospital transportation department by using VRPSTW

There is a recent growing interest in recognition of biological individuals in images: human being detection, face detection, animal body recognition are examples of relatively new research areas with important practical applications. From the Computer Vision pointof view, humans and animals are nonrigid objects, i.e., objects whose shape can undergo nonrigid geometric transformations. Other nonrigid interesting objects are: robots, tools, vehicles, etc. Nonrigid object recognition is a difficult task and an open research problem in which we need to deal with both the common problems of the rigid case and the variability derived from the degrees of freedom of the nonrigid object's shape. In this paper we present a survey on the methodological features of the main existing approaches to nonrigid object recognition showing how systems specialized in different domains often share the same techniques. We will focus our attention on the two most important application domains: human body recognition and face detection, which provide most of the scientific proposals concerning nonrigid object recognition.

Deformable registration of digital images

This paper proposes a novel elastic model and presents a deformable registration method based on the model. The method registers images without the need to extract features from the images, and therefore works directly on grey-level images. A new similarity metric is given on which the formation of external forces is based. The registration method, taking the coarse-to-fine strategy, constructs external forces in larger scales for the first few iterations to rely more on global evidence, and then in smaller scales for later iterations to allow local refinements. The stiffness of the elastic body decreases as the process proceeds. To make it widely applicable, the method is not restricted to any type of transformation. The variations between images are thought as general free-form deformations. Because the elastic model designed is linearized, it can be solved very efficiently with high accuracy. The method has been successfully tested on MRI images. It will certainly find other uses such as matching time-varying sequences of pictures for motion analysis, fitting templates into images for non-rigid object recognition, matching stereo images for shape recovery, etc.

Video compression implemented using rigid and non-rigid object recognition and computer graphics animation

A video compression prototype system that significantly reduces the required bandwidth for video transmission by using intelligent terminals is described. The terminals used for video compression are trained to recognize what they see. Each computer contains a three-dimensional description of objects that might be in the scene. This description includes information about the objects, such as how they move and how they are constrained. The terminal is trained to recognize certain individuals, locations, and objects that have had their features scanned into an image library. The image library contains the physical description of the objects and description of how they change and move over time.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>