3,970 research outputs found
Efficient Segmentation with Texture in Ore Images Based on Box-supervised Approach
Image segmentation methods have been utilized to determine the particle size
distribution of crushed ores. Due to the complex working environment,
high-powered computing equipment is difficult to deploy. At the same time, the
ore distribution is stacked, and it is difficult to identify the complete
features. To address this issue, an effective box-supervised technique with
texture features is provided for ore image segmentation that can identify
complete and independent ores. Firstly, a ghost feature pyramid network
(Ghost-FPN) is proposed to process the features obtained from the backbone to
reduce redundant semantic information and computation generated by complex
networks. Then, an optimized detection head is proposed to obtain the feature
to maintain accuracy. Finally, Lab color space (Lab) and local binary patterns
(LBP) texture features are combined to form a fusion feature similarity-based
loss function to improve accuracy while incurring no loss. Experiments on MS
COCO have shown that the proposed fusion features are also worth studying on
other types of datasets. Extensive experimental results demonstrate the
effectiveness of the proposed method, which achieves over 50 frames per second
with a small model size of 21.6 MB. Meanwhile, the method maintains a high
level of accuracy compared with the state-of-the-art approaches on ore image
dataset. The source code is available at
\url{https://github.com/MVME-HBUT/OREINST}.Comment: 14 pages, 8 figure
Advances in Image Processing, Analysis and Recognition Technology
For many decades, researchers have been trying to make computersâ analysis of images as effective as the system of human vision is. For this purpose, many algorithms and systems have previously been created. The whole process covers various stages, including image processing, representation and recognition. The results of this work can be applied to many computer-assisted areas of everyday life. They improve particular activities and provide handy tools, which are sometimes only for entertainment, but quite often, they significantly increase our safety. In fact, the practical implementation of image processing algorithms is particularly wide. Moreover, the rapid growth of computational complexity and computer efficiency has allowed for the development of more sophisticated and effective algorithms and tools. Although significant progress has been made so far, many issues still remain, resulting in the need for the development of novel approaches
Designing a fruit identification algorithm in orchard conditions to develop robots using video processing and majority voting based on hybrid artificial neural network
The first step in identifying fruits on trees is to develop garden robots for different purposes
such as fruit harvesting and spatial specific spraying. Due to the natural conditions of the fruit
orchards and the unevenness of the various objects throughout it, usage of the controlled conditions
is very difficult. As a result, these operations should be performed in natural conditions, both
in light and in the background. Due to the dependency of other garden robot operations on the
fruit identification stage, this step must be performed precisely. Therefore, the purpose of this
paper was to design an identification algorithm in orchard conditions using a combination of video
processing and majority voting based on different hybrid artificial neural networks. The different
steps of designing this algorithm were: (1) Recording video of different plum orchards at different
light intensities; (2) converting the videos produced into its frames; (3) extracting different color
properties from pixels; (4) selecting effective properties from color extraction properties using
hybrid artificial neural network-harmony search (ANN-HS); and (5) classification using majority
voting based on three classifiers of artificial neural network-bees algorithm (ANN-BA), artificial
neural network-biogeography-based optimization (ANN-BBO), and artificial neural network-firefly
algorithm (ANN-FA). Most effective features selected by the hybrid ANN-HS consisted of the third
channel in hue saturation lightness (HSL) color space, the second channel in lightness chroma hue
(LCH) color space, the first channel in L*a*b* color space, and the first channel in hue saturation
intensity (HSI). The results showed that the accuracy of the majority voting method in the best execution
and in 500 executions was 98.01% and 97.20%, respectively. Based on different performance evaluation
criteria of the classifiers, it was found that the majority voting method had a higher performance.European Union (EU) under Erasmus+ project entitled
âFostering Internationalization in Agricultural Engineering in Iran and Russiaâ [FARmER] with grant
number 585596-EPP-1-2017-1-DE-EPPKA2-CBHE-JPinfo:eu-repo/semantics/publishedVersio
Global and local characterization of rock classification by Gabor and DCT filters with a color texture descriptor
In the automatic classification of colored natural textures, the idea of
proposing methods that reflect human perception arouses the enthusiasm of
researchers in the field of image processing and computer vision. Therefore,
the color space and the methods of analysis of color and texture, must be
discriminating to correspond to the human vision. Rock images are a typical
example of natural images and their analysis is of major importance in the rock
industry. In this paper, we combine the statistical (Local Binary Pattern (LBP)
with Hue Saturation Value (HSV) and Red Green Blue (RGB) color spaces fusion)
and frequency (Gabor filter and Discrete Cosine Transform (DCT)) descriptors
named respectively Gabor Adjacent Local Binary Pattern Color Space Fusion
(G-ALBPCSF) and DCT Adjacent Local Binary Pattern Color Space Fusion
(D-ALBPCSF) for the extraction of visual textural and colorimetric features
from direct view images of rocks. The textural images from the two G-ALBPCSF
and D-ALBPCSF approaches are evaluated through similarity metrics such as Chi2
and the intersection of histograms that we have adapted to color histograms.
The results obtained allowed us to highlight the discrimination of the rock
classes. The proposed extraction method provides better classification results
for various direct view rock texture images. Then it is validated by a
confusion matrix giving a low error rate of 0.8% of classification
Computational Modeling for Abnormal Brain Tissue Segmentation, Brain Tumor Tracking, and Grading
This dissertation proposes novel texture feature-based computational models for quantitative analysis of abnormal tissues in two neurological disorders: brain tumor and stroke. Brain tumors are the cells with uncontrolled growth in the brain tissues and one of the major causes of death due to cancer. On the other hand, brain strokes occur due to the sudden interruption of the blood supply which damages the normal brain tissues and frequently causes death or persistent disability. Clinical management of these brain tumors and stroke lesions critically depends on robust quantitative analysis using different imaging modalities including Magnetic Resonance (MR) and Digital Pathology (DP) images. Due to uncontrolled growth and infiltration into the surrounding tissues, the tumor regions appear with a significant texture variation in the static MRI volume and also in the longitudinal imaging study. Consequently, this study developed computational models using novel texture features to segment abnormal brain tissues (tumor, and stroke lesions), tracking the change of tumor volume in longitudinal images, and tumor grading in MR images. Manual delineation and analysis of these abnormal tissues in large scale is tedious, error-prone, and often suffers from inter-observer variability. Therefore, efficient computational models for robust segmentation of different abnormal tissues is required to support the diagnosis and analysis processes. In this study, brain tissues are characterized with novel computational modeling of multi-fractal texture features for multi-class brain tumor tissue segmentation (BTS) and extend the method for ischemic stroke lesions in MRI. The robustness of the proposed segmentation methods is evaluated using a huge amount of private and public domain clinical data that offers competitive performance when compared with that of the state-of-the-art methods. Further, I analyze the dynamic texture behavior of tumor volume in longitudinal imaging and develop post-processing frame-work using three-dimensional (3D) texture features. These post-processing methods are shown to reduce the false positives in the BTS results and improve the overall segmentation result in longitudinal imaging. Furthermore, using this improved segmentation results the change of tumor volume has been quantified in three types such as stable, progress, and shrinkage as observed by the volumetric changes of different tumor tissues in longitudinal images. This study also investigates a novel non-invasive glioma grading, for the first time in literature, that uses structural MRI only. Such non-invasive glioma grading may be useful before an invasive biopsy is recommended. This study further developed an automatic glioma grading scheme using the invasive cell nuclei morphology in DP images for cross-validation with the same patients. In summary, the texture-based computational models proposed in this study are expected to facilitate the clinical management of patients with the brain tumors and strokes by automating large scale imaging data analysis, reducing human error, inter-observer variability, and producing repeatable brain tumor quantitation and grading
Discrete language models for video retrieval
Finding relevant video content is important for producers of television news, documentanes and commercials. As digital video collections become more widely available, content-based video retrieval tools will likely grow in importance for an even wider group of users. In this thesis we investigate language modelling approaches, that have been the focus of recent attention within the text information retrieval community, for the video search task. Language models are smoothed discrete generative probability distributions generally of text and provide a neat information retrieval formalism that we believe is equally applicable to traditional visual features as to text. We propose to model colour, edge and texture histogrambased features directly with discrete language models and this approach is compatible with further traditional visual feature representations. We provide a comprehensive and robust empirical study of smoothing methods, hierarchical semantic and physical structures, and fusion methods for this language modelling approach to video retrieval. The advantage of our approach is that it provides a consistent, effective and relatively efficient model for video retrieval
Polyp Segmentation with Fully Convolutional Deep Neural NetworksâExtended Evaluation Study
Analysis of colonoscopy images plays a significant role in early detection of colorectal cancer. Automated tissue segmentation can be useful for two of the most relevant clinical target applicationsâlesion detection and classification, thereby providing important means to make both processes more accurate and robust. To automate video colonoscopy analysis, computer vision and machine learning methods have been utilized and shown to enhance polyp detectability and segmentation objectivity. This paper describes a polyp segmentation algorithm, developed based on fully convolutional network models, that was originally developed for the Endoscopic Vision Gastrointestinal Image Analysis (GIANA) polyp segmentation challenges. The key contribution of the paper is an extended evaluation of the proposed architecture, by comparing it against established image segmentation benchmarks utilizing several metrics with cross-validation on the GIANA training dataset. Different experiments are described, including examination of various network configurations, values of design parameters, data augmentation approaches, and polyp characteristics. The reported results demonstrate the significance of the data augmentation, and careful selection of the methodâs design parameters. The proposed method delivers state-of-the-art results with near real-time performance. The described solution was instrumental in securing the top spot for the polyp segmentation sub-challenge at the 2017 GIANA challenge and second place for the standard image resolution segmentation task at the 2018 GIANA challenge
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