This work presents a geometry-based image retrieval approach for multi-object images. We commence with developing an effective shape matching method for closed boundaries. Then, a structured representation, called curvature tree (CT), is introduced to extend the shape matching approach to handle images containing multiple objects with possible holes. We also propose an algorithm, based on Gestalt principles, to detect and extract high-level boundaries (or envelopes), which may evolve as a result of the spatial arrangement of a group of image objects. At first, a shape retrieval method using triangle-area representation (TAR) is presented for non-rigid shapes with closed boundaries. This representation is effective in capturing both local and global characteristics of a shape, invariant to translation, rotation, scaling and shear, and robust against noise and moderate amounts of occlusion. For matching, two algorithms are introduced. The first algorithm matches concavity maxima points extracted from TAR image obtained by thresholding the TAR. In the second matching algorithm, dynamic space warping (DSW) is employed to search efficiently for the optimal (least cost) correspondence between the points of two shapes. Experimental results using the MPEG-7 CE-1 database of 1400 shapes show the superiority of our method over other recent methods. Then, a geometry-based image retrieval system is developed for multi-object images. We model both shape and topology of image objects including holes using a structured representation called curvature tree (CT). To facilitate shape-based matching, the TAR of each object and hole is stored at the corresponding node in the CT. The similarity between two CTs is measured based on the maximum similarity subtree isomorphism (MSSI) where a one-to-one correspondence is established between the nodes of the two trees. Our matching scheme agrees with many recent findings in psychology about the human perception of multi-object images. Two algorithms are introduced to solve the MSSI problem: an approximate and an exact. Both algorithms have polynomial-time computational complexity and use the DSW as the similarity measure between the attributed nodes. Experiments on a database of 13500 medical images and a database of 1580 logo images have shown the effectiveness of the proposed method. The purpose of the last part is to allow for high-level shape retrieval in multi-object images by detecting and extracting the envelope of high-level object groupings in the image. Motivated by studies in Gestalt theory, a new algorithm for the envelope extraction is proposed that works in two stages. The first stage detects the envelope (if exists) and groups its objects using hierarchical clustering. In the second stage, each grouping is merged using morphological operations and then further refined using concavity tree reconstruction to eliminate odd concavities in the extracted envelope. Experiment on a set of 110 logo images demonstrates the feasibility of our approach.

Finite Element Analysis during Shape Rolling Processes ABSTRACT This paper used three-dimensional finite element code-deform to analyze the deformation behavior of material at the rolling-gap during shape rolling of T-profiled and H-profiled sheet. The rigid-plastic model was used. The rolls are assumed to be rigid body and the change of temperate during rolling is ignored. In T-profiled sheet rolling, 1-pass and 2-pass rolling were included. There-dimensional deformation of the sheet was analyzed and the filling ratio at the roll gap, the spread of the sheet, rolling force, the curvature of products were predicted etc. Comparison between analytical and experimental results was made to verify the suitability of the DEFORM software. In H-profiled sheet rolling , the stress, strain, velocity distribution of material was investigated and the filling ratio was analyzed at the H shape flange during the different reductions. These results can offer knowledge for the design of actual H-profiled sheet rolling.

New materials always have the possibility of revolutionizing manufacturing processes and the way we live. Bronze, steel alloys, vulcanized rubber, ceramics, and fiber optic cables are just of few of the materials man has discovered which improved his quality of life. One of the more recent additions to the field of material science are materials that exhibit what is known as the shape memory effect. Both metals and synthetic polymers can acquire this property through processing and chemistry. However while shape memory polymers hold a lot of promise, it will require more research and development to make them affordable and useful in large scale applications. / text

<p>The fundamental aim of this study is to examine whether itis possible to automatically convert vector-based drawings toproduct models. The reason fordoing this is that the newobject-based systems cannot make use of the information storedin CAD drawings, which limits the usability of thesesystems.</p><p>Converting paper drawings to vector-format is used today andprovides recognition of lines and text, but does not interpretwhat the shapes represent. A language for describing thegeometrical representations that could be processed directlyinto a recognition program for building elements is missing. Itis easier to describe how to recognize a line as a series ofdots in a raster image, than it is to describe how a complexsymbol of a building element looks like.</p><p>The approach in this research work has been to testdifferent shape recognition algorithms. The proposed method canbe divided into four processes: grouping of geometricalprimitives, classifying these groups, interpreting the contentand analyzing the relationships between the groups. Thealgorithms developed here are based on research within relateddomains, such as pattern recognition and artificialintelligence.</p><p>The algorithms have been developed in a prototypeimplementation and were tested with three layer-structureddrawings used in practice. The results of the tests show thatthere are no crucial obstacles to recognizing a large part ofthe symbols of building elements in a CAD drawing. Therequirement is that the recognition system is able todifferentiate one from another and be tolerant of errors andvariations in the shapes.</p><p><b>Keywords:</b>Shape recognition, shape interpretation,product models</p>

Shape from Shading, Occlusion and Texture are three important sources of depth information. We review and summarize work done on these modules.

We report on initial experiments with an implemented learning system whose inputs are images of two-dimensional shapes. The system first builds semantic network descriptions of shapes based on Brady's smoothed local symmetry representation. It learns shape models form them using a substantially modified version of Winston's ANALOGY program. A generalization of Gray coding enables the representation to be extended and also allows a single operation, called ablation, to achieve the effects of many standard induction heuristics. The program can learn disjunctions, and can learn concepts suing only positive examples. We discuss learnability and the pervasive importance of representational hierarchies.

The goal of low-level vision is to estimate an underlying scene, given an observed image. Real-world scenes (e.g., albedos or shapes) can be very complex, conventionally requiring high dimensional representations which are hard to estimate and store. We propose a low-dimensional representation, called a scene recipe, that relies on the image itself to describe the complex scene configurations. Shape recipes are an example: these are the regression coefficients that predict the bandpassed shape from bandpassed image data. We describe the benefits of this representation, and show two uses illustrating their properties: (1) we improve stereo shape estimates by learning shape recipes at low resolution and applying them at full resolution; (2) Shape recipes implicitly contain information about lighting and materials and we use them for material segmentation.

This work presents a geometry-based image retrieval approach for multi-object images. We commence with developing an effective shape matching method for closed boundaries. Then, a structured representation, called curvature tree (CT), is introduced to extend the shape matching approach to handle images containing multiple objects with possible holes. We also propose an algorithm, based on Gestalt principles, to detect and extract high-level boundaries (or envelopes), which may evolve as a result of the spatial arrangement of a group of image objects. At first, a shape retrieval method using triangle-area representation (TAR) is presented for non-rigid shapes with closed boundaries. This representation is effective in capturing both local and global characteristics of a shape, invariant to translation, rotation, scaling and shear, and robust against noise and moderate amounts of occlusion. For matching, two algorithms are introduced. The first algorithm matches concavity maxima points extracted from TAR image obtained by thresholding the TAR. In the second matching algorithm, dynamic space warping (DSW) is employed to search efficiently for the optimal (least cost) correspondence between the points of two shapes. Experimental results using the MPEG-7 CE-1 database of 1400 shapes show the superiority of our method over other recent methods. Then, a geometry-based image retrieval system is developed for multi-object images. We model both shape and topology of image objects including holes using a structured representation called curvature tree (CT). To facilitate shape-based matching, the TAR of each object and hole is stored at the corresponding node in the CT. The similarity between two CTs is measured based on the maximum similarity subtree isomorphism (MSSI) where a one-to-one correspondence is established between the nodes of the two trees. Our matching scheme agrees with many recent findings in psychology about the human perception of multi-object images. Two algorithms are introduced to solve the MSSI problem: an approximate and an exact. Both algorithms have polynomial-time computational complexity and use the DSW as the similarity measure between the attributed nodes. Experiments on a database of 13500 medical images and a database of 1580 logo images have shown the effectiveness of the proposed method. The purpose of the last part is to allow for high-level shape retrieval in multi-object images by detecting and extracting the envelope of high-level object groupings in the image. Motivated by studies in Gestalt theory, a new algorithm for the envelope extraction is proposed that works in two stages. The first stage detects the envelope (if exists) and groups its objects using hierarchical clustering. In the second stage, each grouping is merged using morphological operations and then further refined using concavity tree reconstruction to eliminate odd concavities in the extracted envelope. Experiment on a set of 110 logo images demonstrates the feasibility of our approach.

The fundamental aim of this study is to examine whether itis possible to automatically convert vector-based drawings toproduct models. The reason fordoing this is that the newobject-based systems cannot make use of the information storedin CAD drawings, which limits the usability of thesesystems. Converting paper drawings to vector-format is used today andprovides recognition of lines and text, but does not interpretwhat the shapes represent. A language for describing thegeometrical representations that could be processed directlyinto a recognition program for building elements is missing. Itis easier to describe how to recognize a line as a series ofdots in a raster image, than it is to describe how a complexsymbol of a building element looks like. The approach in this research work has been to testdifferent shape recognition algorithms. The proposed method canbe divided into four processes: grouping of geometricalprimitives, classifying these groups, interpreting the contentand analyzing the relationships between the groups. Thealgorithms developed here are based on research within relateddomains, such as pattern recognition and artificialintelligence. The algorithms have been developed in a prototypeimplementation and were tested with three layer-structureddrawings used in practice. The results of the tests show thatthere are no crucial obstacles to recognizing a large part ofthe symbols of building elements in a CAD drawing. Therequirement is that the recognition system is able todifferentiate one from another and be tolerant of errors andvariations in the shapes. <b>Keywords:</b>Shape recognition, shape interpretation,product models / NR 20140805

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