191,412 research outputs found
Some variants of Hausdorff distance for word matching
Several recently proposed modifications of Hausdorff distance (HD) are examined with respect to word image matching for bad quality typewritten Bulgarian text. The main idea of these approaches presumes that omission of the extreme distances between the points of the compared images eliminates the noise (to some extent) and the algorithms become more robust. A few robust HD measures, namely, censored HD, LTS-HD, and
a new binary image comparison method that uses a windowed Hausdorff distance, lie in the base of the computer
experiments carried out using 54 pages of typewritten text
Analysis of feature detector and descriptor combinations with a localization experiment for various performance metrics
The purpose of this study is to provide a detailed performance comparison of
feature detector/descriptor methods, particularly when their various
combinations are used for image-matching. The localization experiments of a
mobile robot in an indoor environment are presented as a case study. In these
experiments, 3090 query images and 127 dataset images were used. This study
includes five methods for feature detectors (features from accelerated segment
test (FAST), oriented FAST and rotated binary robust independent elementary
features (BRIEF) (ORB), speeded-up robust features (SURF), scale invariant
feature transform (SIFT), and binary robust invariant scalable keypoints
(BRISK)) and five other methods for feature descriptors (BRIEF, BRISK, SIFT,
SURF, and ORB). These methods were used in 23 different combinations and it was
possible to obtain meaningful and consistent comparison results using the
performance criteria defined in this study. All of these methods were used
independently and separately from each other as either feature detector or
descriptor. The performance analysis shows the discriminative power of various
combinations of detector and descriptor methods. The analysis is completed
using five parameters: (i) accuracy, (ii) time, (iii) angle difference between
keypoints, (iv) number of correct matches, and (v) distance between correctly
matched keypoints. In a range of 60{\deg}, covering five rotational pose points
for our system, the FAST-SURF combination had the lowest distance and angle
difference values and the highest number of matched keypoints. SIFT-SURF was
the most accurate combination with a 98.41% correct classification rate. The
fastest algorithm was ORB-BRIEF, with a total running time of 21,303.30 s to
match 560 images captured during motion with 127 dataset images.Comment: 11 pages, 3 figures, 1 tabl
Robust Adaptive Median Binary Pattern for noisy texture classification and retrieval
Texture is an important cue for different computer vision tasks and
applications. Local Binary Pattern (LBP) is considered one of the best yet
efficient texture descriptors. However, LBP has some notable limitations,
mostly the sensitivity to noise. In this paper, we address these criteria by
introducing a novel texture descriptor, Robust Adaptive Median Binary Pattern
(RAMBP). RAMBP based on classification process of noisy pixels, adaptive
analysis window, scale analysis and image regions median comparison. The
proposed method handles images with high noisy textures, and increases the
discriminative properties by capturing microstructure and macrostructure
texture information. The proposed method has been evaluated on popular texture
datasets for classification and retrieval tasks, and under different high noise
conditions. Without any train or prior knowledge of noise type, RAMBP achieved
the best classification compared to state-of-the-art techniques. It scored more
than under impulse noise densities, more than under
Gaussian noised textures with standard deviation , and more than
under Gaussian blurred textures with standard deviation .
The proposed method yielded competitive results and high performance as one of
the best descriptors in noise-free texture classification. Furthermore, RAMBP
showed also high performance for the problem of noisy texture retrieval
providing high scores of recall and precision measures for textures with high
levels of noise
Clinical feasibility of quantitative ultrasound texture analysis: A robustness study using fetal lung ultrasound images
OBJECTIVES: To compare the robustness of several methods based on quantitative ultrasound (US) texture analysis to evaluate its feasibility for extracting features from US images to use as a clinical diagnostic tool. METHODS: We compared, ranked, and validated the robustness of 5 texture-based methods for extracting textural features from US images acquired under different conditions. For comparison and ranking purposes, we used 13,171 non-US images from widely known available databases (OUTEX [University of Oulu, Oulu, Finland] and PHOTEX [Texture Lab, Heriot-Watt University, Edinburgh, Scotland]), which were specifically acquired under different controlled parameters (illumination, resolution, and rotation) from 103 textures. The robustness of those methods with better results from the non-US images was validated by using 666 fetal lung US images acquired from singleton pregnancies. In this study, 2 similarity measurements (correlation and Chebyshev distances) were used to evaluate the repeatability of the features extracted from the same tissue images. RESULTS: Three of the 5 methods (gray-level co-occurrence matrix, local binary patterns, and rotation-invariant local phase quantization) had favorably robust performance when using the non-US database. In fact, these methods showed similarity values close to 0 for the acquisition variations and delineations. Results from the US database confirmed robustness for all of the evaluated methods (gray-level co-occurrence matrix, local binary patterns, and rotation-invariant local phase quantization) when comparing the same texture obtained from different regions of the image (proximal/distal lungs and US machine brand stratification). CONCLUSIONS: Our results confirmed that texture analysis can be robust (high similarity for different condition acquisitions) with potential to be included as a clinical tool
Shape Similarity Measurement for Known-Object Localization: A New Normalized Assessment
International audienceThis paper presents a new, normalized measure for assessing a contour-based object pose. Regarding binary images, the algorithm enables supervised assessment of known-object recognition and localization. A performance measure is computed to quantify differences between a reference edge map and a candidate image. Normalization is appropriate for interpreting the result of the pose assessment. Furthermore, the new measure is well motivated by highlighting the limitations of existing metrics to the main shape variations (translation, rotation, and scaling), by showing how the proposed measure is more robust to them. Indeed, this measure can determine to what extent an object shape differs from a desired position. In comparison with 6 other approaches, experiments performed on real images at different sizes/scales demonstrate the suitability of the new method for object-pose or shape-matching estimation
Binary Patterns Encoded Convolutional Neural Networks for Texture Recognition and Remote Sensing Scene Classification
Designing discriminative powerful texture features robust to realistic
imaging conditions is a challenging computer vision problem with many
applications, including material recognition and analysis of satellite or
aerial imagery. In the past, most texture description approaches were based on
dense orderless statistical distribution of local features. However, most
recent approaches to texture recognition and remote sensing scene
classification are based on Convolutional Neural Networks (CNNs). The d facto
practice when learning these CNN models is to use RGB patches as input with
training performed on large amounts of labeled data (ImageNet). In this paper,
we show that Binary Patterns encoded CNN models, codenamed TEX-Nets, trained
using mapped coded images with explicit texture information provide
complementary information to the standard RGB deep models. Additionally, two
deep architectures, namely early and late fusion, are investigated to combine
the texture and color information. To the best of our knowledge, we are the
first to investigate Binary Patterns encoded CNNs and different deep network
fusion architectures for texture recognition and remote sensing scene
classification. We perform comprehensive experiments on four texture
recognition datasets and four remote sensing scene classification benchmarks:
UC-Merced with 21 scene categories, WHU-RS19 with 19 scene classes, RSSCN7 with
7 categories and the recently introduced large scale aerial image dataset (AID)
with 30 aerial scene types. We demonstrate that TEX-Nets provide complementary
information to standard RGB deep model of the same network architecture. Our
late fusion TEX-Net architecture always improves the overall performance
compared to the standard RGB network on both recognition problems. Our final
combination outperforms the state-of-the-art without employing fine-tuning or
ensemble of RGB network architectures.Comment: To appear in ISPRS Journal of Photogrammetry and Remote Sensin
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