Amit Thakur

Bio: Amit Thakur is an academic researcher. The author has contributed to research in topics: Digital image processing & Binary image. The author has an hindex of 1, co-authored 3 publications receiving 5 citations.

Object recognition from image using grid based color moments feature extraction method

Abstract: Image processing is a mechanism to convert an image into digital form and perform various operations on it, in order to get an enhanced image or to extract some useful information from it. In Image processing system, it treats images as two dimensional signals while applying number of image processing methods to them. This leads to an increasing number of generated digital images. Therefore it is required automatic systems to recognize the objects from the images. These systems may collect the number of features of a image and specification of image and consequently the different features of an object will identify the object from the image. Image processing is among rapidly growing technologies today, with its applications in various aspects of a business. Image Processing forms core research area within engineering and computer science disciplines too. Its most common and effective method is retrieve the textual features from various methods. But most of the methods do not yield the more accurate features form the image. So there is a requirement of an effective and efficient method for features extraction from the image. Moreover, images are rich in content, so some approaches are proposed based on various features derived directly from the content of the image: these are the grid-based-color-moments (GBCM) approaches. They allow users to search the desired object from image by specifying visual features (e.g., colour, texture and shape). Once the features have been defined and extracted, the retrieval becomes a task of measuring similarity between image features. In this paper, we have proposed a number of existing methods for GBCM applications.

Domestic Sewage Waste Water Treatment by using Electro-Coagulation Process

Abstract: In, India huge quantity of Domestic Waste Water is generated, and remains untreated. These waters find their way into the nearby rivers, ponds, lakes, etc. and thus cause pollution of water sources. Most of the existing domestic waste water treatment plants uses old technologies for treatment and the treated water from these plants have limited usages. This paper presents the difference in treated water quality obtained from a conventional Sewage Treatment plant and the other obtained from an electro-coagulation method of treatment. It also analyses the benefits of electro-coagulation method of domestic waste water treatment over the conventional methods of treatment.

Identification of Object Using GBSM Method and Classification Using Support Vector Machine

Abstract: In Image processing system, it treats images as two dimensional signals while applying number of image processing methods to them. This leads to an increasing number of generated digital images. Therefore it is required automatic systems to recognize the objects from the images. These systems may collect the number of features of a image and specification of image and consequently the different features of an object will identify the object from the image. Image processing is among rapidly growing technologies today, with its applications in various aspects of a business. Image Processing forms core research area within engineering and computer science disciplines too. It’s most common and effective method is retrieve the textual features from various methods. But most of the methods do not yield the more accurate features form the image. So there is a requirement of an effective and efficient method for features extraction from the image. An image is also thought-about to contain sub-images typically cited as regions-of-interest, ROIs, or just regions. The foremost needs for image process of pictures is that the photographs be obtainable in digitized type, that is, arrays of finite length binary words. For conversion, the given Image is sampled on a separate grid and every sample or component is quantal employing a finite variety of bits. The digitized image is processed by a pc. To show a digital image, it's initial reborn into analog signal, that is scanned onto a show.

Texture Feature On Determining Quantity of Soil Organic Matter For Patchouli Plant Using Backpropagation Neural Network

Abstract: Patchouli (Pogostemon Cablin Bent) has higher PA (Patchouli Alcohol) and oil production if grown in soil containing 75% organic matter. One way that can be used to detect the content of organic matter is to use soil images. The problem in the use of soil images is the color of the soil that is almost similar, namely the gradation between dark brown to black. Therefore, color features are not enough to be used as input in the recognition process. For this purposes, texture features are added in this study in addition to color features. The color features are extracted using color moment and the texture features are extracted using Gray Level Co-occurrence Matrix (GLCM). These feature was then chosen to get the best combination as input in the identification process using the Backpropagation Neural Network (BPNN). The system identifies the quantity of soil organic matter into five classes, namely very low, low, medium, high, and very high. The highest accuracy result obtained was 73% and MSE value 0.5122 by using five GLCM features (Angular Second Moment, contrast, correlation, Inverse Difference Moment, and entropy). This result was obtained by using the BPNN parameter, namely learning rate values 0.5, maximum iteration values of 1000, number training data 210, and total test data 12.

Real Time Pest Detection and Identification Using Image Processing

Abstract: Timely pest detection and identification in agricultural crops is essential to ensure good production. This action helps fight the pests and also reduce the use of pesticides. There exist different techniques which areused for detection and identification of bio-agressors, the major one being image processing. Image processing involves capturing a static or dynamic image and applying various preprocessing techniques to the image to highlight and detect the object in the image or motion in case of video analysis.

Parallel processors for high speed multiple objects recognition

Abstract: In this work, we introduce a novel method for recognizing multiple objects in an image at a very high speed. The system is based on self learning high speed parallel processing devices. The system processes video streams at speed of 1000 frames per second or more. For high speed object recognition using sequential computing from an image of a video having thousands of frames per second and each image frame consists of thousands of pixels, we need very much time for executing complicated algorithms. In the traditional way of computing and recognizing, systems are very time consuming compared to our system because the traditional systems use sequential computation for recognizing, with some complicated functions. If we use other types of parallel processors like ANN for processing each pixel or group of pixels, those systems need programming and giving data to such large number of processors are practically difficult. Here we have used a self learning parallel processor device which is made for doing some kinds of particular jobs. This parallel processing devices are easy to manipulate and can be trained simultaneously. It contains memory for storing data comparators for comparing with previously stored memory etc. Training as well as functioning are in real time even if the system process thousands of image frames per second.

Complementary Methods for Human Visual Perception of Visual Weather Lore Sky Objects Using Machine Learning Methods

Abstract: Recent research indicate a surge in the use of machine learning and artificial intelligence to compliment the processes of human visual perception. In particular, applying closeness measures of digital objects is of great significance in the attempts to account for the correspondence between digitized sky objects and some human identifiable object. The scoring of computerized objects can be based on testing a combination of well-known features humans use for visual perception, with a consideration that the human visual cognition system is well tailored for discriminating structural information from visual objects. This way, benchmark tests can be used to compute some proximity of detected objects to the specified object’s reality. Apart from producing outputs for use in the predictions, object similarity tests can also act as a mechanism for quality assessment process for the results of computer object detectors. One assumption here is that similar objects cannot qualify as perfect matches to their real objects but may contain some acceptable divergence in their closeness. In this paper, algorithms for extracting shape, color and texture information in visual sky (specific to traditional weather lore) objects are investigated as candidates for visual sky objects benchmarking, and their performances compared using a collection of positive/negative instances of visual sky objects. The rationale for testing both positive/negative instances was due to the fact that while the sky objects detectors can be expected to generate positive detections, the number of false positives detectable should be negligible.