47 research outputs found

    Ventral-stream-like shape representation : from pixel intensity values to trainable object-selective COSFIRE models

    Get PDF
    Keywords: hierarchical representation, object recognition, shape, ventral stream, vision and scene understanding, robotics, handwriting analysisThe remarkable abilities of the primate visual system have inspired the construction of computational models of some visual neurons. We propose a trainable hierarchical object recognition model, which we call S-COSFIRE (S stands for Shape and COSFIRE stands for Combination Of Shifted FIlter REsponses) and use it to localize and recognize objects of interests embedded in complex scenes. It is inspired by the visual processing in the ventral stream (V1/V2 → V4 → TEO). Recognition and localization of objects embedded in complex scenes is important for many computer vision applications. Most existing methods require prior segmentation of the objects from the background which on its turn requires recognition. An S-COSFIRE filter is automatically configured to be selective for an arrangement of contour-based features that belong to a prototype shape specified by an example. The configuration comprises selecting relevant vertex detectors and determining certain blur and shift parameters. The response is computed as the weighted geometric mean of the blurred and shifted responses of the selected vertex detectors. S-COSFIRE filters share similar properties with some neurons in inferotemporal cortex, which provided inspiration for this work. We demonstrate the effectiveness of S-COSFIRE filters in two applications: letter and keyword spotting in handwritten manuscripts and object spotting in complex scenes for the computer vision system of a domestic robot. S-COSFIRE filters are effective to recognize and localize (deformable) objects in images of complex scenes without requiring prior segmentation. They are versatile trainable shape detectors, conceptually simple and easy to implement. The presented hierarchical shape representation contributes to a better understanding of the brain and to more robust computer vision algorithms.peer-reviewe

    Нейросетевая технология распознавания объектов топологии интегральных микросхем

    Get PDF
    Рассмотрены методы обработки изображений и распознавания на основе нейронных сетей, ориентированные на применение в системах технического зрения проектирования интегральных микросхем. Представлена структура системы, реализующей нейросетевую технологию распознавания объектов топологии интегральных микросхем.Methods and algorithms for image processing and recognition based on neural network are considered , that are applied to computer vision systems for CAD of integrated circuits. A structure of a computer vision software system is proposed implemented neural network technology of Object Recognition on Integrated Circuits Layouts

    The Prototype of Software System of Neural Network Control of Telemetry Data

    Get PDF
    This paper describes a prototype of software system of neural network control of telemetry data for malfunction diagnosis of spacecraft subsystems. The prototype is used for testing of intelligent technologies for processing information about a spacecraft subsystems state, prediction and detection of irregularities of the spacecraft subsystem modes. The Information obtained from on-board data sources on space communication channel is used for processing

    Intelligent classification of ammonia concentration based on odor profile

    Get PDF
    This thesis presents the intelligent classification of ammonia concentration based on the standard of oil and gas industries wastewater discharge. The intelligent classification using signal processing is a well-known technique in many applications and as well in the oil and gas industry. The intelligent classification technique for ammonia concentration classification is a demanding technique especially in the environmental sector. Ammonia solution properties and ammonia solution preparations were studied in this thesis which commonly used in industry. The objectives of this thesis are to develop an intelligence classification of ammonia concentration based on the oil and gas industry wastewater discharge schedule and to analyze performance of the intelligent classification of ammonia concentration based on the oil and gas industry wastewater discharge schedule. In this thesis the ammonia odor profile has been pre-identified by chemist using four sensor array. The ammonia concentration was validated using a commercialized gas sensor and spectrophotometer to cross-validated e-nose instrument. The odor profile from two different samples; high (20 ppm and 25 ppm) and low (5 ppm, 10 ppm and 1 5ppm) concentration that have been normalized and visualized in a 2D plot to extract the unique patterns. The variance of the low and high concentration of ammonia odor profile has been identified as different group samples. This group samples have been analyzed statistically using Boxplot, calibration curve and proximity matrix, The thesis describes the statistical techniques to visualize the pattern and using mean features to classify between the low and high concentration. Two intelligent classification techniques have been used which are Artificial Neural Network (ANN) using the back-propagation approaches and then, the result of ANN model was cross-validated.using CBR. Both ANN model and CBR classifier have been measured using several performance measures. From the results, it is observed that ANN model and CBR classifier are capable of classifying 100% of ammonia concentration odor profile from the water. The results can also significantly reduce the cost and time, and improve product reliability and customer confidence
    corecore