Brain Tumour Detection using Deep Learning Technique
AI Driven Detection and Remediation of Diabetic Foot Ulcer(DFU)
Advancements in Image Processing: Towards Near-Reversible Data Hiding and Enhanced Dehazing Using Deep Learning
State-of-the-Art Deep Learning Techniques for Object Identification in Practical Applications
Landslide Susceptibility Mapping through Weightages Derived from Statistical Information Value Model
An Efficient Foot Ulcer Determination System for Diabetic Patients
Statistical Wavelet based Adaptive Noise Filtering Technique for MRI Modality
Real Time Sign Language: A Review
Remote Sensing Schemes Mingled with Information and Communication Technologies (ICTS) for Flood Disaster Management
FPGA Implementation of Shearlet Transform Based Invisible Image Watermarking Algorithm
A Comprehensive Study on Different Pattern Recognition Techniques
User Authentication and Identification Using NeuralNetwork
Flexible Generalized Mixture Model Cluster Analysis withElliptically-Contoured Distributions
Efficient Detection of Suspected areas in Mammographic Breast Cancer Images
This paper presents a novel feature extraction method, Local Orientation Gradient XoR Patterns (LOGXoRP) for image indexing and retrieval. The LOGXoRP encodes the exclusive OR (XOR) operation between the center pixel and its surrounding neighbors of quantized orientation and gradient values, whereas the Local Binary Patterns (LBP) and the Local Gradient Patterns (LGP) encode the relationship between the gray values of center pixel and its neighbors. The authors shows that the LOGXoRP can extract effective texture (edge) features as compared to LBP and LGP. The performance of the proposed method is tested by conducting two experiments on Corel-5K and Corel-10K databases. The results of the proposed method after being investigated shows a significant improvement in terms of their evaluation measures as compared to LBP, LGP and other existing state-of-art techniques on respective databases.
Textile industries are one of the major revenue generating industries in Tamil Nadu, India. Greater efforts are taken in manufacturing the good quality fabrics. Defects in a fabric are a major issue to the textile industry. Textile industries in Tamil Nadu initially had only manual inspection strategy for the detection of faults. Later, automation has been made through image processing techniques. Traditional inspection process for fabric defects is by human visual inspection, which is inefficient, costly and time consuming. To enhance the accuracy of fabric defects detection, and help people out from this tedious and stressful work, an automated fabric inspection system has been proposed. To automate this process, the fault present on the fabrics can be identified using MATLAB with Image processing techniques and the implementation of this idea is done in Arduino kit for real time applications.
Leaves are one of the important parts in a tree. Extracting accurately the shape of a leaf is a crucial step in image-based systems. The partial or total absence of textures on leaf surface and the high color variability of leaves belonging to the same species make shape as the main recognition element. For such reasons, leaf segmentation plays a decisive role in the leaf extraction process. Even though many general segmentation methods have been proposed in the last decades, leaf segmentation presents specific challenges. In particular, a pixel-level precision is required in order to highlight fine scale boundary structures and discriminate similar global shapes. The authors propose a robust and accurate method for segmenting objects acquired under various controlled conditions. Then, they have improved the performance of the segmentation methods using preprocessing tools such as, color distance map and input strokes. Based on these methods, we can eliminate unwanted boundaries and localize the leaf object efficiently. They have implemented a Hybrid Guided Active Contour (GAC) method to measure geometric properties of leaf images, and have provided with a comparative study for various segmentation algorithms based on performance metrics. Based on experimental results, GAC provides improved performance in leaf datasets.
Handwritten Character Recognition is a crucial part of Optical Character Recognition (OCR) through which the computer understands the handwriting of individuals automatically from the image of a handwritten script. From a decade, OCR becomes the most important application of Pattern Recognition, Machine Vision and Signal Processing for the rapid growth of technology, which can be described as the Electronic or Mechanical conversion of the captured or scanned image. The image is converted into the machine encoded form that can be further used in machine translation, text to speech conversion, text mining and the storage of data. Selections of appropriate feature extraction and classification methods are the crucial factors for achieving a higher rate of recognition with greater level of accuracy for handwritten characters to accurately achieve recognition of each and every letter. Here, in this paper the authors attempt to give a more elaborative image for a comprehensive review that has been proposed to achieve a deep study of the handwritten characters recognition, and this data will be useful for the readers working in the field of handwritten character recognition.
Pattern Acceptance has been admired because of its advancement in the appliance areas. The applying breadth includes medicine, communications, automation, aggressive intelligence, abstracts mining, bioinformatics, certificate classification, accent recognition, business and abounding others. In this analysis, cardboard assorted approaches of Arrangement Acceptance has been presented with their pros-cons, and the appliance specific archetype has been confirmed. From the base of the survey, arrangement acceptance techniques could be categorized into six parts. Such awning techniques include Neural Network scheme, Statistics Techniques, Template Matching, Hybrid versions and Fuzzy Model.
