Difference of Gaussian Oriented Gradient Histogram for Face Sketch to Photo Matching

Setumin, Samsul; Suandi, Shahrel Azmin

doi:10.1109/access.2018.2855208

Cited by 22 publications

(28 citation statements)

References 32 publications

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“…Computer vision assisted techniques have also been utilized in materials science for microstructure characterization and recognition [1]- [3], including powder characterization for additive manufacturing [4]. Computer vision feature detection techniques such as Harris-Laplace [5], Difference of Gaussian [6], Haralick texture features [7], and histogram of oriented gradients [1] have been previously utilized. In particular, the "bag of visual words" image representation employed by Holm et al [8] to create "fingerprint" microstructures is a good example of using computer vision techniques to extract information from micrograph images.…”

Section: Related Workmentioning

confidence: 99%

Predicting compressive strength of consolidated molecular solids using computer vision and deep learning

et al. 2020

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We explore the application of computer vision and machine learning (ML) techniques to predict material properties (e.g., compressive strength) based on SEM images of material microstructure. We show that it is possible to train ML models to predict materials performance based on SEM images alone, demonstrating this capability on the real-world problem of predicting uniaxially compressed peak stress of consolidated molecular solids (i.e. TATB) samples. Our image-based ML approach reduces root mean square error (RMSE) by an average of 51% over a non-image-based baseline. We compared two complementary approaches to this problem: (1) a traditional ML approach, random forest (RF), using state-ofthe-art computer vision features and (2) an end-to-end deep learning (DL) approach, where features are learned automatically from raw images. We demonstrate the complementarity of these approaches, showing that RF performs best in the "small data" regime in which many real-world scientific applications reside (up to 24% lower RMSE than DL), whereas DL outpaces RF in the "big data" regime, where abundant training samples are available (up to 24% lower RMSE than RF).

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Section: Related Workmentioning

confidence: 99%

Predicting compressive strength of consolidated molecular solids using computer vision and deep learning

et al. 2020

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“…A common representation of the face sketch and photo is extracted and matched. It extracts discriminative features that are invariant to photo and sketch modalities before performing similarity measure [15]- [19], [26], [44]. Klare and Jain [14] proposed a local feature extraction approach using Scale Invariant Feature Transform (SIFT) descriptor.…”

Section: Related Workmentioning

confidence: 99%

“…In order to ensure a better retrieval rate, there are two observed problems that require proper treatment: illumination difference and shape exaggeration. To tackle the illumination problem, we propose so that the image is represented by Difference of Gaussian Oriented Gradient Histogram (Do-GOGH) feature [26]. This is because DoGOGH has been proven to perform effectively on matching face sketch to photo with illumination effects [26].…”

Section: Patch Of Interest Dynamic Local Feature Matchingmentioning

confidence: 99%

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Canonical Correlation Analysis Feature Fusion With Patch of Interest: A Dynamic Local Feature Matching for Face Sketch Image Retrieval

2020

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An automatic photo retrieval system based on a face sketch has very useful application as to narrow down potential suspects in criminal investigations. This is true when there is no other evident except the face sketch that is rendered based on the recollection of a victim or eyewitness. Among the noticeable difficulties in matching the sketch and photo due to its modality difference are the generated sketch has some tendency of shape exaggeration, the sketch has very less accurate details and the real-world photo may expose to lighting variation unlike the sketch. In this paper, we attempt to address these complications by matching the sketch and photos using dynamic local feature of Difference of Gaussian Oriented Gradient Histogram (DoGOGH) on some selected patches. To avoid discriminative power degradation due to a large number of gallery images, two stage matching blocks are introduced in a cascaded fashion. The front block matches the feature such that it short lists k most similar photos for the second block. In this front block, Histogram of Oriented Gradient (HOG) and Gabor Wavelet (GW) features are fused by maximizing the correlation between the two using Canonical Correlation Analysis (CCA). Based on the short listed photos, the following block re-matched the sketch and photos using dynamically extracted local feature on its Patch of Interest (PoI). Eventually, the matching scores from the blocks are fused before getting rank-1 accuracy. The experimental results on two baseline datasets indicate that the proposed method outperforms the stateof-the-art methods. The extended evaluation on semi-forensic and forensic sketch datasets demonstrate its usage feasibility.

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“…Preprocessing is mainly used to reduce the noises present in an image and improve the original Fig.3 shows the output of adaptive histogram equalization of the input cotton leaf image.  Differential of Gaussian Difference of Gaussians (DoG) [7] is one of the feature extraction technique. It estimates the blurred version of the original image and removes the same from the another.…”

Section: A Pre-processingmentioning

confidence: 99%

Cotton Leaf Disease Detection Using Texture and Gradient Features

Afroze,

Beham,

Tamilselvi

et al. 2019

IJEAT

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The detection of cotton leaf disease is a very important factor to prevent serious outbreak. Most cotton diseases are caused by fungi, bacteria, and insects. A new method is proposed for careful detection of diseases and timely handling to prevent the crops from heavy losses. A disease due to bacteria, insects and fungus occurs in the cotton leaves in the range of about 80-95%. In the proposed work, first the group of infected leaves and normal leaves are collected and the image preprocessing is done using Adaptive histogram equalization for enhancing the contrast. In feature extraction phase, texture and gradient feature are extracted using Local Binary Pattern (LBP), Histogram of Oriented Gradient (HOG) and Differential of Gaussian (DOG). K- Nearest neighbor classifier is applied to classify the leaf image as a unaffected or an affected leaf. A cotton leaf database is internally created to evaluate the efficacy of our algorithm. The validate results show that the proposed method achieved higher classification accuracy in lower computational time.

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Difference of Gaussian Oriented Gradient Histogram for Face Sketch to Photo Matching

Cited by 22 publications

References 32 publications

Predicting compressive strength of consolidated molecular solids using computer vision and deep learning

Predicting compressive strength of consolidated molecular solids using computer vision and deep learning

Canonical Correlation Analysis Feature Fusion With Patch of Interest: A Dynamic Local Feature Matching for Face Sketch Image Retrieval

Cotton Leaf Disease Detection Using Texture and Gradient Features

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