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Management of crops from early stage to mature harvest stage involves identification and monitoring of plant diseases, nutrient deficiency, controlled irrigation and controlled use of fertilizers and pesticides. Although the number of remote sensing solutions is increasing, the availability and ground visibility during critical growth stages of crops continue to be major concerns. eAGROBOT (a prototype)...
The nose radius of a cutting insert is known to affect the surface roughness and dimensional accuracy of the finished product. Thus, accurate measurement of tool nose radius is important in tool quality monitoring. In this paper we present a new approach in measuring nose radii of multiple cutting inserts from scanned images using image processing. Batch processing of inserts solves the problem of...
This paper presents an improved vision-based algorithm for detecting and recognizing vehicle logos in images captured by road surveillance cameras. Vehicle logo recognition is quite a challenging task considering the low resolution of the logos, the wide range of variability in illumination and the interference of the air-intake grille. However, our system, assessed on a set of 1386 vehicle images...
The structured light techniques consisting of a light pattern with a regular structure in have been used widely for depth sensing. Traditional structured light pattern using the pattern with the fixed density and intensity is difficult to obtain the accuracy depth data. In this paper, We propose a depth sensing method of complex scenes by using a multimodal pattern consisting of structure lights with...
We address a classification method for motor imagery tasks-based brain computer interface (BCI). The wavelet coefficients are used to extract the features from the motor imagery electroencephalographic (EEG) signals and the k-nearest neighbor classifier is applied to classify the pattern of left or right hand imagery movement and rest. The performance of the proposed method is evaluated using EEG...
Despite the advent and popularity of low-cost commercial sensors (e.g., Microsoft Kinect), research in 3D vision still primarily focuses on the development of advanced algorithms geared towards high resolution data. This paper presents a comparative performance evaluation of renowned state-of-the-art 3D local surface descriptors for the task of registration of both high and low resolution range image...
The Canny algorithm has been extensively adopted to perform edge detection in images. The Derivative of Gaussian (DoG) proposed by Canny has been shown to be the optimal edge detector to compute the image gradient due to its robustness to noise. However, the DoG has some important drawbacks in relation to images with thin edges of a few pixels width and junctions. The excessive blurring provided by...
In this paper, we implement a facial landmarking system to improve the performance of landmark location accuracy for the tree-structured based facial detector proposed recently by Zhu and Ramanan. Our main objective is to overcome their limitation where very small faces could not be detected and landmarked. Furthermore, we also want to improve the landmarking accuracy and reduce false positive rate...
The histogram specification turns a shape of a histogram into that we want to specify. This technique can be applied usefully in various image processing fields such as machine vision. However, the histogram specification technique has its basic limits. For instance, the histogram does not have location information of pixels. Also, the accuracy of specification drops because of quantization error...
Graph-based representation has an effective and extensive usage in pattern recognition due to represent properties of entities and binary relations at the same time. But a major drawback of graphs is lack of basic and essential mathematical operations required in many algorithms of pattern recognition. To overcome this problem, graph embedding in vector space enables classical statistical learning...
The performance of optical character recognition (OCR) algorithm is poor on low resolution scanned text images. The conventional low pass filters in L2 space can slightly improve the performance. The method of enhancement of poor resolution text images using a low pass signal filtering algorithm in the weighted Sobolev space results in high pass correction similar to un sharp masking. This can further...
In this paper, we present a method to detect changes in high resolution remote sensing images based on superparsing proposed by Tighe et al. By comparing with several superpixel segmentation methods, we choose the SLIC (Simple Linear Iterative Clustering) method which can keep image boundary, produce consistent superpixels with similar size and shape, and also calculates fast. After superpixel segmentation,...
In this paper, we propose a method for matching biometric data from disparate domains. Specifically, we focus on the problem of comparing a low-resolution (LR) image with a high-resolution (HR) one. Existing coupled mapping methods do not fully exploit the HR information or they do not simultaneously use samples from both domains during training. To this end, we propose a method that learns coupled...
Many applications along the manuscript analysis pipeline rely on the accuracy of pre-processing steps. Perfectly detecting the main text area in ancient historical documents is of great importance for these applications. We propose a learning-free approach to detect the main text area in ancient manuscripts. First, we coarsely segment the main text area by using a texture-based filter. Then, we refine...
Repeat-pass interferometry is an extension of Synthetic Aperture Sonar (SAS) that exploits the temporal characteristics of seafloor reverberation by coherently combining echoes from multiple passes of the sonar platform. In order to achieve pass-to-pass coherence, the spatial separation between passes must be less than a value known as the critical baseline, which depends on the sonar parameters and...
An approach to the generation of super-resolution (SR) images from fundoscopy images is proposed that is based on the 3D registration of the original fundoscopy images. The proposed approach utilizes a simple 3D registration method to enable the application of conventional SR techniques which, otherwise, employ 2D image registration. Qualitative and quantitative comparative evaluation shows that the...
Automated behaviour recognition is a challenging problem and it has recently gained momentum in biological behaviour studies. This paper describes a framework for tracking and automatical classification of the behaviour of multiple freely interacting Drosophila Melanogaster (fruit flies) in a low resolution video. The movements of interacting flies are recorded by Fly world, a dedicated imaging platform...
Forest species recognition has been traditionally addressed as a texture classification problem, and explored using standard texture methods such as Local Binary Patterns (LBP), Local Phase Quantization (LPQ) and Gabor Filters. Deep learning techniques have been a recent focus of research for classification problems, with state-of-the art results for object recognition and other tasks, but are not...
This paper exploits the concept of sparseness to generate novel contextual multi-resolution texture descriptors. We propose to extract low-dimension features from Gabor-filtered images by considering the sparseness of filter bank responses. We construct several texture descriptors: the basic version describes each pixel by its contextual textural sparseness, while other versions also integrate multi-resolution...
Accurate character recognition is still very important for camera based printed document analysis. Due to its inherent conceptual and technical simplicity, conventional recognition strategies relied on features extracted using square block zoning of a character image. In this paper, we propose an isotropic feature extraction method using regular hexagonal zoning and confirm its effectiveness by printed...
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