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Showing 1 to 20 of 287 for “"image classification"”.
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Image Classification With Unstructured Collections
… vision consider the analysis of a single image or video. However, there is also a long history in multi-view analysis. Having information from multiple views can provide many benefits, such as estimating depth, mitigating issues of occlusion, and generally providing more information about …
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Image classification and feature selection
Made available in DSpace on 2012-06-27T21:22:52Z (GMT). No. of bitstreams: 9 Chen_Gang.pdf: 2139361 bytes, checksum: 3e14f03bb001785a473bc6193dc54f8b (MD5) license.txt: 4058 bytes, checksum: 50cddeb1b191bb1536258b22551f35da (MD5) ch4.tex: 87063 bytes, checksum: 8fac0f031e2c4b465952bb040f6131fa …
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Deep Learning on Hyperspectral Image Classification
Classification of Hyperspectral images is one of the main problems in the research field of Remote Sensing and other applications developed through computer vision. With the advantage of spectral and spatial information, it is possible to distinguish effectively different materials on the surface. …
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Hierarchical density estimation for image classification
… (GMMs) have been widely used in patch-based image classification problems. Despite the satisfactory results reported, both methods suffer from a number of disadvantages. For instance, a histogram may be easy to learn but has a large quantization error; on the contrary, Gaussian mixture model …
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Image Classification with Consistent Supporting Evidence
Adoption of machine learning models in healthcare requires end users’ trust in the system. Models that provide additional supportive evidence for their predictions promise to facilitate adoption. We define consistent evidence to be both compatible and sufficient with respect to model predictions. …
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Transfer learning algorithms for image classification
An ideal image classifier should be able to exploit complex high dimensional feature representations even when only a few labeled examples are available for training. To achieve this goal we develop transfer learning algorithms that: 1) Leverage unlabeled data annotated with meta-data and 2) …
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Learning visual concepts for image classification
Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1999.
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Scale and texture in digital image classification
… study on textural properties of digital images. Spatial information exists at a hierarchy of scales and texture is a consequence of the objects in that hierarchy. Within-class texture results from the spatial arrangement of objects at the next finer level in the hierarchy than the …
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Naturally Generated Decision Trees for Image Classification
Image classification has been a pivotal area of research in Deep Learning, with a vast body of literature working to tackle the problem, constantly striving to achieve higher accuracies. This push to reach achieve greater prediction accuracy however, has further exacerbated the black box phenomenon …
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Image Classification using Gabor Filters and Machine Learning
Feature extraction and classification are important areas of research in image processing and computer vision with a myriad of applications in science and industry. The focus of this work is on the robust classification of tree and non-tree areas in aerial imagery of the eastern Andes mountains in …
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Chest X-Ray Image Classification with Deep Learning
… one essential task in CAD, the chest X-ray (CXR) image classification problem, with the deep learning technologies from the following three aspects. First, considering most diseases existing in CXRs usually happen in small, localized areas, we propose to localize the local discriminative regions …
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DATA MINING AND IMAGE CLASSIFICATION USING GENETIC PROGRAMMING
… the application of GP for data mining and image processing. Knowledge discovery and data mining have been widely used in business, healthcare, and scientific fields. In data mining, classification is supervised learning that identifies new patterns and maps the data to predefined targets. A …
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Applied Plankton Image Classification for Imaging FlowCytobot Data
… flow cytometer, produces high resolution images of plankton at rates up to 10 Hz for months or years, resulting in billions of images. This project compares various methods to categorize incoming images of plankton gathered by the IFCB - Convolutional Neural Nets (CNNs), Vision …
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Image classification using color cues and texture orientation
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1996.
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Building and using robust representations in image classification
… of correctly classified inputs causing model misclassification--is a prominent indication of such shortcomings. The goal of this thesis is to work towards building learned representations that are more robust and human-aligned. To achieve this, we turn to adversarial (or robust) training, an …
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Graph-based quantum neural networks for satellite image classification
… au matériel. Ce mémoire utilise des QNN pour la classification binaire d’images satellites multispec trales. Ces images capturent la réflectance sur plusieurs bandes spectrales, telles que le rouge, le vert, le bleu, l’infrarouge proche et l’infrarouge à ondes courtes, perme ttant d’identifier …
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Deep neural network models for image classification and regression
… is to list the objects that likely exist in an image that is grabbed by a visually impaired person, To this end, we proceed by extracting several features from the respective query image in order to capture the textural as well as the chromatic cues therein. Further, in order to improve the …
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Accelerating research on 3D medical image classification and regression
Contains fulltext : 315690.pdf (Publisher’s version ) (Open Access)
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Visual feature learning with application to medical image classification
… features have been explored for medical image classification, which include SIFT and Local Binary Patterns (LBP). However, hand-crafted features may not be optimally discriminative for classifying images from particular domains (e.g. colonoscopy), as not necessarily tuned to the domain’s …
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