A Binary Neural Shape Matcher using Johnson Counters and Chain Codes

In this paper, we introduce a neural network-based shape matching algorithm that uses Johnson Counter codes coupled with chain codes. Shape matching is a fundamental requirement in content-based image retrieval systems. Chain codes describe shapes using sequences of numbers. They are simple and flexible. We couple this power with the efficiency and flexibility of a binary associative-memory neural network. We focus on the implementation details of the algorithm when it is constructed using the neural network. We demonstrate how the binary associative-memory neural network can index and match chain codes where the chain code elements are represented by Johnson codes

FPGA-Based Multimodal Embedded Sensor System Integrating Low- and Mid-Level Vision

Motion estimation is a low-level vision task that is especially relevant due to its wide range of applications in the real world. Many of the best motion estimation algorithms include some of the features that are found in mammalians, which would demand huge computational resources and therefore are not usually available in real-time. In this paper we present a novel bioinspired sensor based on the synergy between optical flow and orthogonal variant moments. The bioinspired sensor has been designed for Very Large Scale Integration (VLSI) using properties of the mammalian cortical motion pathway. This sensor combines low-level primitives (optical flow and image moments) in order to produce a mid-level vision abstraction layer. The results are described trough experiments showing the validity of the proposed system and an analysis of the computational resources and performance of the applied algorithms

A Simulated shape recognition system using feature extraction

A simulated shape recognition system using feature extraction was built as an aid for designing robot vision systems. The simulation allows the user to study the effects of image resolution and feature selection on the performance of a vision system that tries to identify unknown 2-D objects. Performance issues that can be studied include identification accuracy and recognition speed as functions of resolution and the size and makeup of the feature set. Two approaches to feature selection were studied as was a nearest neighbor classification algorithm based on Mahalanobis distances. Using a pool of ten objects and twelve features, the system was tested by performing studies of hypothetical visual recognition tasks

Real-time On-board Object Tracking for Cooperative Flight Control

One of possible cooperative Situations for flights could be a scenario when the decision on a new path is taken by A Certain fleet member, who is called the leader. The update on the new path is Transmitted to the fleet members via communication That can be noisy. An optical sensor can be used as a back-up for re-estimating the path parameters based on visual information. For A Certain topology, the issue can be solved by continuous tracking of the leader of the fleet in the video sequence and re-adjusting parameters of the flight, accordingly. To solve such a problem of a real time system has been developed for Recognizing and tracking 3D objects. Any change in the 3D position of the leading object is Determined by the on-board system and adjustments of the speed, pitch, yaw and roll angles are made to sustain the topology. Given a 2D image acquired by an on-board camera, the system has to perform the background subtraction, recognize the object, track it and evaluate the relative rotation, scale and translation of the object. In this paper, a comparative study of different algorithms is Carried out based on time and accuracy constraints. The solution for 3D pose estimation is provided based on the system of invariant Zernike moments. The candidate techniques solving the complete set of procedures have been Implemented on Texas Instruments TMS320DM642 EVM board. It is shown That 14 frames per second can be processed; That supports the real time Implementation of the tracking system with the reasonable accuracy

Quantized Compressive K-Means

The recent framework of compressive statistical learning aims at designing tractable learning algorithms that use only a heavily compressed representation-or sketch-of massive datasets. Compressive K-Means (CKM) is such a method: it estimates the centroids of data clusters from pooled, non-linear, random signatures of the learning examples. While this approach significantly reduces computational time on very large datasets, its digital implementation wastes acquisition resources because the learning examples are compressed only after the sensing stage. The present work generalizes the sketching procedure initially defined in Compressive K-Means to a large class of periodic nonlinearities including hardware-friendly implementations that compressively acquire entire datasets. This idea is exemplified in a Quantized Compressive K-Means procedure, a variant of CKM that leverages 1-bit universal quantization (i.e. retaining the least significant bit of a standard uniform quantizer) as the periodic sketch nonlinearity. Trading for this resource-efficient signature (standard in most acquisition schemes) has almost no impact on the clustering performances, as illustrated by numerical experiments