Computer recognition of partially-occluded objects.

by Chan Ming-hong.Bibliography: leaves 67-68Thesis (M.Ph.)--Chinese University of Hong Kong, 198

Classification and Grip of Occluded Objects

The present paper exposes a system for detection, classification, and grip of occluded objects by machine vision, artificial intelligence, and an anthropomorphic robot, to generate a solution for the subjection of elements that present occlusions. The deep learning algorithm used is based on Convolutional Neural Networks (CNN), specifically Fast R-CNN (Fast Region-Based CNN) and DAG-CNN (Directed Acyclic Graph CNN) for pattern recognition, the three-dimensional information of the environment was collected through Kinect V1, and tests simulations by the tool VRML. A sequence of detection, classification, and grip was programmed to determine which elements present occlusions and which type of tool generates the occlusion. According to the user's requirements, the desired elements are delivered (occluded or not), and the unwanted elements are removed. It was possible to develop a program with 88.89% accuracy in gripping and delivering occluded objects using networks Fast R-CNN and DAG-CNN with achieving of 70.9% and 96.2% accuracy respectively, detecting elements without occlusions for the first net and classifying the objects into five tools (Scalpel, Scissor, Screwdriver, Spanner, and Pliers), with the second net. The grip of occluded objects requires accurate detection of the element located at the top of the pile of objects to remove it without affecting the rest of the environment. Additionally, the detection process requires that a part of the occluded tool be visible to determine the existence of occlusions in the stac

Recognition of partially occluded threat objects using the annealed Hopefield network

Recognition of partially occluded objects has been an important issue to airport security because occlusion causes significant problems in identifying and locating objects during baggage inspection. The neural network approach is suitable for the problems in the sense that the inherent parallelism of neural networks pursues many hypotheses in parallel resulting in high computation rates. Moreover, they provide a greater degree of robustness or fault tolerance than conventional computers. The annealed Hopfield network which is derived from the mean field annealing (MFA) has been developed to find global solutions of a nonlinear system. In the study, it has been proven that the system temperature of MFA is equivalent to the gain of the sigmoid function of a Hopfield network. In our early work, we developed the hybrid Hopfield network (HHN) for fast and reliable matching. However, HHN doesn't guarantee global solutions and yields false matching under heavily occluded conditions because HHN is dependent on initial states by its nature. In this paper, we present the annealed Hopfield network (AHN) for occluded object matching problems. In AHN, the mean field theory is applied to the hybird Hopfield network in order to improve computational complexity of the annealed Hopfield network and provide reliable matching under heavily occluded conditions. AHN is slower than HHN. However, AHN provides near global solutions without initial restrictions and provides less false matching than HHN. In conclusion, a new algorithm based upon a neural network approach was developed to demonstrate the feasibility of the automated inspection of threat objects from x-ray images. The robustness of the algorithm is proved by identifying occluded target objects with large tolerance of their features

Multi-layer Architecture For Storing Visual Data Based on WCF and Microsoft SQL Server Database

In this paper we present a novel architecture for storing visual data. Effective storing, browsing and searching collections of images is one of the most important challenges of computer science. The design of architecture for storing such data requires a set of tools and frameworks such as SQL database management systems and service-oriented frameworks. The proposed solution is based on a multi-layer architecture, which allows to replace any component without recompilation of other components. The approach contains five components, i.e. Model, Base Engine, Concrete Engine, CBIR service and Presentation. They were based on two well-known design patterns: Dependency Injection and Inverse of Control. For experimental purposes we implemented the SURF local interest point detector as a feature extractor and $K$-means clustering as indexer. The presented architecture is intended for content-based retrieval systems simulation purposes as well as for real-world CBIR tasks.Comment: Accepted for the 14th International Conference on Artificial Intelligence and Soft Computing, ICAISC, June 14-18, 2015, Zakopane, Polan

From Stereogram to Surface: How the Brain Sees the World in Depth

When we look at a scene, how do we consciously see surfaces infused with lightness and color at the correct depths? Random Dot Stereograms (RDS) probe how binocular disparity between the two eyes can generate such conscious surface percepts. Dense RDS do so despite the fact that they include multiple false binocular matches. Sparse stereograms do so even across large contrast-free regions with no binocular matches. Stereograms that define occluding and occluded surfaces lead to surface percepts wherein partially occluded textured surfaces are completed behind occluding textured surfaces at a spatial scale much larger than that of the texture elements themselves. Earlier models suggest how the brain detects binocular disparity, but not how RDS generate conscious percepts of 3D surfaces. A neural model predicts how the layered circuits of visual cortex generate these 3D surface percepts using interactions between visual boundary and surface representations that obey complementary computational rules.Air Force Office of Scientific Research (F49620-01-1-0397); National Science Foundation (EIA-01-30851, SBE-0354378); Office of Naval Research (N00014-01-1-0624

Recognition of occluded objects using curvature

New approaches of object representation reliable for partially occluded objects recognition are introduced in this article. Objects are represented by their boundaries, which are deformed by the occlusion. The boundary representation was made by approximation with circle arcs. The representation was designed to be local and robust to occlusion. The curve approximation with circle arcs is equivalent to the curvature representation with respect to noise. The algorithm is simple and easy to implement. Experimental results are presented

KLASIFIKASI ONLINE CITRA DAUN BERDASARKAN FITUR BENTUK DAN RUAS DAUN

Sistem temu kembali citra untuk aplikasi pengklasifikasian citra daun berdasarkan bentuk dan ruas daun sangat dibutuhkan dalam ilmu botani. Paper ini menjelaskan tentang suatu metode pengenalan jenis daun berdasarkan input berupa sketsa bentuk daun yang dibandingkan terhadap database sketsa daun yang ada. Sistem ini dapat dilakukan secara online sehingga dapat diakses dimanapun dan kapanpun user berada. Metode pengenalan ini meliputi Minimum Perimeter Polygon untuk ekstraksi fitur bentuk pada citra daun, Venation Mathing untuk ekstraksi fitur ruas daun pada citra daun. Untuk mendukung kinerja dua metode diatas, penulis memperkenalkan metode Windowing matrix untuk mendeteksi ujung dan pangkal ruas daun, Thinning Algorithm menjadikan ruas daun lebih sederhana (lining one pixel), dan Rotation Invariant untuk mengubah orientasi daun menjadi tegak lurus. Setelah melalui uji coba dan analisa, dapat disimpulkan bahwa hasil pencarian dengan menggunakan metode diatas mampu menghasilkan F1 Score atau tingkat akurasi 95.83%, Recall sebesar 100%, dan Precision sebesar 92.59%. Kata Kunci: Klasifikasi, online, system temu kembali, fitur bentuk daun, fitur ruas daun, Minimum Perimeter Polygon, Venation Matching, Windowing Matrix, Thinning Algorithm, Rotation Invariant

A detection method of intersections for determining overlapping using active vision

Sometimes, the presence of objects difficult the observation of other neighboring objects. This is because part of the surface of an object occludes partially the surface of another, increasing the complexitiy in the recognition process. Therefore, the information which is acquired from scene to describe the objects is often incomplete and depends a great deal on the view point of the observation. Thus, when any real scene is observed, the regions and the boundaries which delimit and dissociate objects from others are not perceived easily. In this paper, a method to discern objects from others, delimiting where the surface of each object begins and finishes is presented. Really, here, we look for detecting the overlapping and occlusion zones of two or more objects which interact among each other in a same scene. This is very useful, on the one hand, to distinguish some objects from others when the features like texture colour and geometric form are not sufficient to separate them with a segmentation process. On the other hand, it is also important to identify occluded zones without a previous knowledge of the type of objects which are wished to recognize. The proposed approach is based on the detection of occluded zones by means of structured light patterns projected on the object surfaces in a scene. These light patterns determine certain discontinuities of the beam projections when they hit against the surfaces becoming deformed themselves. So that, such discontinuities are taken like zones of boundary of occlusion candidate regions