6 research outputs found
Evaluating color texture descriptors under large variations of controlled lighting conditions
The recognition of color texture under varying lighting conditions is still
an open issue. Several features have been proposed for this purpose, ranging
from traditional statistical descriptors to features extracted with neural
networks. Still, it is not completely clear under what circumstances a feature
performs better than the others. In this paper we report an extensive
comparison of old and new texture features, with and without a color
normalization step, with a particular focus on how they are affected by small
and large variation in the lighting conditions. The evaluation is performed on
a new texture database including 68 samples of raw food acquired under 46
conditions that present single and combined variations of light color,
direction and intensity. The database allows to systematically investigate the
robustness of texture descriptors across a large range of variations of imaging
conditions.Comment: Submitted to the Journal of the Optical Society of America
Colorectal Cancer Tissue Classification Based on Machine Learning
For digital pathology, automatic recognition of different tissue types in histological images is important for diagnostic assistance and healthcare. Since histological images generally contain more than one tissue type, multi-class texture analysis plays a critical role to solve this problem. This study examines the important statistical features including Gray Level Co-occurrence Matrix (GLCM), Discrete Wavelet Transform (DWT), Spatial filters, Wiener filter, Gabor filters, Haralick features, fractal filters, and local binary pattern (LBP) for colorectal cancer tissue identification by using support vector machine (SVM) and decision fusion of feature selection. The average experimental results achieve high identification rate which is significantly superior to the existing known methods. In summary, the proposed method based on machine learning outperforms the techniques described in the literatures and achieve high classification accuracy rate at 93.17% for eight classes and 96.02% for ten classes which demonstrate promising applications for cancer tissue classification of histological image
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
Visibility graphs for image processing.
The family of image visibility graphs (IVG/IHVG) have been recently introduced as simple algorithms by which scalar fields can be mapped into graphs. Here we explore the usefulness of such operator in the scenario of image processing and image classication. We demonstrate that the link architecture of the image visibility graphs encapsulates relevant information on the structure of the images and we explore their potential as image filters. We introduce several graph features, including the novel concept of Visibility Patches, and show through several examples that these features are highly informative, computationally efficient and universally applicable for general pattern recognition and image classication tasks
Mobile-cloud assisted video summarization framework for efficient management of remote sensing data generated by wireless capsule sensors
YesWireless capsule endoscopy (WCE) has great advantages over traditional endoscopy
because it is portable and easy to use, especially in remote monitoring health-services.
However, during the WCE process, the large amount of captured video data demands a
significant deal of computation to analyze and retrieve informative video frames. In order to
facilitate efficient WCE data collection and browsing task, we present a resource- and
bandwidth-aware WCE video summarization framework that extracts the representative
keyframes of the WCE video contents by removing redundant and non-informative frames.
For redundancy elimination, we use Jeffrey-divergence between color histograms and
inter-frame Boolean series-based correlation of color channels. To remove non-informative
frames, multi-fractal texture features are extracted to assist the classification using an
ensemble-based classifier. Owing to the limited WCE resources, it is impossible for the
WCE system to perform computationally intensive video summarization tasks. To resolve
computational challenges, mobile-cloud architecture is incorporated, which provides resizable
computing capacities by adaptively offloading video summarization tasks between the client
and the cloud server. The qualitative and quantitative results are encouraging and show that
the proposed framework saves information transmission cost and bandwidth, as well as the
valuable time of data analysts in browsing remote sensing data.Supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2013R1A1A2012904)
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An Investigation into the Performance of Ethnicity Verification Between Humans and Machine Learning Algorithms
There has been a significant increase in the interest for the task of classifying
demographic profiles i.e. race and ethnicity. Ethnicity is a significant human
characteristic and applying facial image data for the discrimination of ethnicity is
integral to face-related biometric systems. Given the diversity in the application
of ethnicity-specific information such as face recognition and iris recognition, and
the availability of image datasets for more commonly available human
populations, i.e. Caucasian, African-American, Asians, and South-Asian Indians.
A gap has been identified for the development of a system which analyses the
full-face and its individual feature-components (eyes, nose and mouth), for the
Pakistani ethnic group. An efficient system is proposed for the verification of the
Pakistani ethnicity, which incorporates a two-tier (computer vs human) approach.
Firstly, hand-crafted features were used to ascertain the descriptive nature of a
frontal-image and facial profile, for the Pakistani ethnicity. A total of 26 facial
landmarks were selected (16 frontal and 10 for the profile) and by incorporating
2 models for redundant information removal, and a linear classifier for the binary
task. The experimental results concluded that the facial profile image of a
Pakistani face is distinct amongst other ethnicities. However, the methodology
consisted of limitations for example, low performance accuracy, the laborious
nature of manual data i.e. facial landmark, annotation, and the small facial image
dataset. To make the system more accurate and robust, Deep Learning models
are employed for ethnicity classification. Various state-of-the-art Deep models
are trained on a range of facial image conditions, i.e. full face and partial-face
images, plus standalone feature components such as the nose and mouth. Since
ethnicity is pertinent to the research, a novel facial image database entitled
Pakistani Face Database (PFDB), was created using a criterion-specific selection
process, to ensure assurance in each of the assigned class-memberships, i.e.
Pakistani and Non-Pakistani. Comparative analysis between 6 Deep Learning
models was carried out on augmented image datasets, and the analysis
demonstrates that Deep Learning yields better performance accuracy compared
to low-level features. The human phase of the ethnicity classification framework
tested the discrimination ability of novice Pakistani and Non-Pakistani
participants, using a computerised ethnicity task. The results suggest that
humans are better at discriminating between Pakistani and Non-Pakistani full
face images, relative to individual face-feature components (eyes, nose, mouth),
struggling the most with the nose, when making judgements of ethnicity. To
understand the effects of display conditions on ethnicity discrimination accuracy, two conditions were tested; (i) Two-Alternative Forced Choice (2-AFC) and (ii)
Single image procedure. The results concluded that participants perform
significantly better in trials where the target (Pakistani) image is shown alongside
a distractor (Non-Pakistani) image. To conclude the proposed framework,
directions for future study are suggested to advance the current understanding of
image based ethnicity verification.Acumé Forensi