nils-herrmann/clean_scirepeval huggingface dataset:
Robust radial distortion correction based on alternate optimization

A robust radial distortion correction method that requires only a single image of the distorted imaged pattern is presented. The method is robust to the orientation of the imaged pattern and does not require the availability of the ideal reference regularly structured pattern which can be recreated from detected features in the distorted image. Radial distortion parameters, i.e., the Center of Distortion (CoD) and radial distortion coefficients (RDC), are optimized by minimizing corresponding cost functions in an alternate manner. Tests with synthetic and real data are presented in order to illustrate the performance robustness of the proposed algorithm.
Multiple description channel-optimized trellis-coded quantization

This paper presents the design of multiple description trellis-coded quantizers that are optimized for transmitting data over multiple noisy channels. A multiple description channel-optimized trellis-coded quantization (MDCOTCQ) scheme is developed whereby all trellis-coded quantizers (side and central) are designed to operate over the binary symmetric channel with a given bit error rate (BER). Using the tensor product of trellises, an expression of the multi-channel transition probability matrix is derived. This formulation enables the construction of multiple description channel-optimized trellis-coded quantizers for arbitrary bit rates and arbitrary channel bit error rates. The proposed MDCOTCQ system enables the transmission of compressed data over noisy diversity or packet-based communication systems, and enables outstanding robustness to channel or packet loss. Examples are presented to illustrate the performance of the proposed MDCOTCQ-based source coder.
A no-reference perceptual texture regularity metric

This paper presents a no reference perceptual metric that quantifies the degree of regularity in textures. The metric is based on the probability of visual attention at each pixel of the texture image, similarity of visual attention of the textural primitives and the periodic spatial distribution of foveated fixation regions throughout the image. It is shown through subjective testing that the proposed metric has a strong correlation with the Mean Opinion Score for the regularity of textures.
Stereo Vision Based Automated Solder Ball Height and Substrate Coplanarity Inspection

Solder ball height and substrate coplanarity inspection is essential to the detection of potential connectivity issues in semi-conductor units. Current ball height and substrate coplanarity inspection tools such as laser profiling, fringe projection, and confocal microscopy are expensive, require complicated setup and are slow, which makes them difficult to use in a real-time manufacturing setting. Therefore, a reliable, in-line ball height and substrate coplanarity measurement method is needed for inspecting units undergoing assembly.
Very low bit-rate target-based image coding

This paper presents a target-based image coding system for very low bit rate, intelligent image coding and transmission. The proposed image coder is a wavelet object-based coder in which the identified more-relevant regions are coded with higher fidelity than the other less-relevant image components. For this purpose, a scalable, multiresolution, shape representation and coding method is developed. The proposed image coder uses a trellis-coded quantizer (TCQ) and an object-based rate allocation scheme for coding the image information. Coding examples are presented to illustrate the performance of the proposed target-based image coder.
An analysis/synthesis model for the human visual system based on subspace decomposition and multirate filter bank theory

A two-stage model of the human visual system (HVS) comprised of low-level (analysis) and a high-level (synthesis) processing stages is presented. The processing done at the low-level stage is described using two approaches, one based on a subspace decomposition and the other on a multirate filter bank. These two models simultaneously provide the high-level processing stage with different perspectives or representations of the retinal image. Based on these representations, the high-level processing stage attempts to perceive the encoded visual information. A single framework, which models the hypercolumn-based structure in the striate cortex and combines the different retinal image representations, is proposed. The model resolves differences among previously proposed HVS models, and suggests the development of image processing algorithms which combine and exploit the information provided by different transformations or representations of the input image.<<ETX>>
A No-Reference Objective Image Sharpness Metric Based on the Notion of Just Noticeable Blur (JNB)

This work presents a perceptual-based no-reference objective image sharpness/blurriness metric by integrating the concept of just noticeable blur into a probability summation model. Unlike existing objective no-reference image sharpness/blurriness metrics, the proposed metric is able to predict the relative amount of blurriness in images with different content. Results are provided to illustrate the performance of the proposed perceptual-based sharpness metric. These results show that the proposed sharpness metric correlates well with the perceived sharpness being able to predict with high accuracy the relative amount of blurriness in images with different content.
An adaptive POCS algorithm for compression artifacts removal

An effective compression artifacts removal algorithm is proposed based on the theory of projections onto convex sets (POCS). It includes a block classification procedure, a ringing detection procedure, prediction of the spatial distribution of the quantization errors and estimation of the visibility of the compression artifacts. Information gained from both the spatial and transform domains, is incorporated into adaptive projections. Experiments performed on JPEG-compressed images, demonstrate the effectiveness of the proposed algorithm in suppressing both blocking and ringing artifacts, as well as the ability of the algorithm to preserve the image sharpness.
Background recovery from multiple images

In this paper we propose an algorithm to extract the background by removing unwanted objects from multiple images of a scene with varying illumination conditions captured by a stationary camera. The variations in illumination from scene to scene are due to the possible presence of different illumination sources and different foreground objects causing different shadows and reflections in each scene. While this causes the background to be non-stationary when considering pixel intensities, the algorithm exploits the fact that the background is static while the foreground is non-stationary in a given feature space. The extracted foreground regions are treated as holes and are filled from one of the available images where the background at the corresponding position is un-occluded. The background pixels for filling the holes are selected based on a cost function that attempts to maximize the naturalness and perceived quality of the reconstructed background.
Two-dimensional FIR filter design by transformation

This article extends the McClellan (1973) transformation method so that new types of one-dimensional (1-D) filters can be transformed, and new types of two-dimensional (2-D) filters can be designed. Two procedures are presented for the design of complex and real, positive- and negative-symmetric, 2-D filters. The first procedure is used to design complex 2-D filters with a rectangular region of support having odd-length sides, whereas the second procedure is used for filters with a rectangular region of support having even-length sides.
Image coding with robust channel-optimized trellis-coded quantization

This paper presents a wavelet-based image coder that is optimized for transmission over the binary symmetric channel (BSC). The proposed coder uses a robust channel-optimized trellis-coded quantization (COTCQ) stage that is designed to optimize the image coding based on the channel characteristics. A phase scrambling stage is also used to further increase the coding performance and robustness to nonstationary signals and channels. The resilience to channel errors is obtained by optimizing the coder performance only at the level of the source encoder with no explicit channel coding for error protection. For the considered TCQ trellis structure, a general expression is derived for the transition probability matrix. In terms of the TCQ encoding rat and the channel bit error rate, and is used to design the COTCQ stage of the image coder. The robust nature of the coder also increases the security level of the encoded bit stream and provides a much more visually pleasing rendition of the decoded image. Examples are presented to illustrate the performance of the proposed robust image coder.
Design of optimal digital FIR filters with arbitrary magnitude and phase responses

This paper presents a new multiple-exchange ascent algorithm for designing optimal Chebyshev digital FIR filters with arbitrary magnitude and phase specifications. Compared to existing Chebyshev design techniques, the new design algorithm exhibits faster convergence while maintaining high accuracy, and is guaranteed to converge to the optimal solution. In addition, the proposed algorithm reduces to the classic second Remez (Parks-McClellan) algorithm when real-only or imaginary-only filters are designed and is, therefore, a true generalization of the classic Remez algorithm to the complex case.
Cell Migration Analysis using a Statistical Level-Set Segmentation on a Wavelet-Based Structure Tensor Feature Space

In this paper, a new noise-resilient cell migration analysis scheme for bladder cancer cells is presented. The proposed scheme is based on texture segmentation in the wavelet domain using structure tensor features and adaptive statistical level-set segmentation. The proposed method extracts the region of interest where the cells are clustering, at different time instances, and computes the overall migration cell rate. For this purpose, the structure tensor data is processed using a trous wavelet filtering, which speeds up the algorithm as compared to existing nonlinear diffusion filters with the same accuracy. The proposed scheme is robust to noise and natural artifacts in the bladder cancer cell images. Moreover, the scheme can be applied successfully to images with poor contrast and high cell concentrations, even when the cells are overlapping and tiny. Simulation results are presented to show the performance of the proposed scheme.
On the design of multidimensional FIR filters by transformation

This paper studies the applicability and limitations of the McClellan transformation method and, as a result, extends this method so that new types of one-dimensional filters can be transformed and new types of multi-dimensional filters can be designed. For this purpose, a new expression for the frequency response of an arbitrary one-dimensional filter is derived in terms of Chebyshev polynomials and other introduced polynomials satisfying recurrence formulae. The main objective is to identify which prototype filters can be transformed, determine what types of symmetry can be designed, and present procedures for transforming the new identified prototypes as well as rules for achieving the possible symmetries.
A No-Reference Texture Regularity Metric Based on Visual Saliency

This paper presents a no-reference perceptual metric that quantifies the degree of perceived regularity in textures. The metric is based on the similarity of visual attention (VA) of the textural primitives and the periodic spatial distribution of foveated fixation regions throughout the image. A ground-truth eye-tracking database for textures is also generated as part of this paper and is used to evaluate the performance of the most popular VA models. Using the saliency map generated by the best VA model, the proposed texture regularity metric is computed. It is shown through subjective testing that the proposed metric has a strong correlation with the mean opinion score for the perceived regularity of textures. The proposed texture regularity metric can be used to improve the quality and performance of many image processing applications like texture synthesis, texture compression, and content-based image retrieval.
Multifeature, Sparse-Based Approach for Defects Detection and Classification in Semiconductor Units

Automated inspection systems play an important role in manufacturing to guarantee higher quality and reduce production costs. In the semiconductor manufacturing industry, assembly and testing processes are getting more complex, resulting in a greater tendency of defects to impact the production process. These defects can cause field failures and can result in customer dissatisfactions and returns. Currently available defect detection and classification systems are customized and hard-wired to the detection of particular classes of defects and cannot deal with new unknown classes of defects. This issue is aggravated by the very small sample size of available anomalies for learning, by the data imbalance problem, since the number of defective samples is significantly much smaller than the number of normal samples, and by the presence of noise. This paper presents a novel multifeature, sparse-based defect detection and classification approach that uses the stacking concept to enhance the classification accuracy. The stacking-based classifier is augmented with a novel adaptive over/downsampling technique to deal with the data imbalance problem. A new pruning technique is proposed to eliminate bad base learners. Shortage of defective units, similarities within different classes of defects, wide variation within the same defect class, and data imbalance are the basic challenges to deal with. Experimental results on real-world data from Intel show that the proposed approach results in a high classification accuracy as compared with the existing methods.Note to Practitioners—The basic motivation of this paper is to design an automated cost-effective, adaptive, and intelligent defect detection and classification system that is easy to train using a small-size sample set of defects and that is robust to noise. The system is scalable in terms of the numbers and types of defects and features, which leads to a shorter development cycle. The presented system is immediately applicable to different types of defects. Inputs of the system are grayscale images. These images are processed to perform defects detection, features extraction, and classification.
Automated analysis of leaf venation patterns

Visual imaging methods have been lately extensively used in applications that are targeted to understand and analyze botanical patterns. There is a rich literature on imaging applications in the above field and various techniques have been developed. In this paper, we introduce a fully automated imaging approach for extracting spatial vein pattern data from leaf images, such as vein densities but also vein reticulation (loops) sizes and shapes. We applied this method to quantify leaf venation patterns of the first rosette leaf of Arabidopsis thaliana throughout a series of developmental stages. In particular, we characterized the size and shape of vein network reticulations, which enlarge and get split by new veins as a leaf develops. For this purpose, the approach mainly uses skeletonization along with other known imaging techniques in an automatic interactive way that enables the user to batch process a high throughput of data.
A Study and Comparison of Human and Deep Learning Recognition Performance under Visual Distortions

Deep neural networks (DNNs) achieve excellent performance on standard classification tasks. However, under image quality distortions such as blur and noise, classification accuracy becomes poor. In this work, we compare the performance of DNNs with human subjects on distorted images. We show that, although DNNs perform better than or on par with humans on good quality images, DNN performance is still much lower than human performance on distorted images. We additionally find that there is little correlation in errors between DNNs and human subjects. This could be an indication that the internal representation of images are different between DNNs and the human visual system. These comparisons with human performance could be used to guide future development of more robust DNNs.
A no reference texture granularity index and application to visual media compression

Texture granularity is an important attribute to quantify the level of details present in the image. This work presents a no-reference texture granularity index and shows using subjective experiments that the proposed granularity index correlates well with the perceived granularity of textures. In addition, a subjective study is conducted to assess the effect of compression on textures with varying degrees of granularity. It is shown that a measure of texture granularity can predict the compression quality.
Joint source-channel coding of images using punctured convolutional codes and trellis-coded quantization

This paper presents a wavelet-based image coder for transmission over the binary symmetric channel (BSC). The proposed coder achieves error resilience by using a joint source-channel coding strategy. The coder uses trellis-coded quantization (TCQ) for source coding, and punctured convolutional (PC) codes for channel coding. The total available bit rate is partitioned into source coding rate and channel coding rate, so as to achieve optimal performance at the given BER.
Image coding for transmission over multiple noisy channels using punctured convolutional codes and trellis-coded quantization

This paper presents a multiple description image coding scheme that facilitates the transmission of digital imagery over multiple noisy channels. The proposed scheme divides the image into smaller parts that are transmitted over the individual channels of an inverse multiplexing system. The division or splitting is done in such a fashion that it facilitates the interpolation of lost coefficients in the case of one or more channel failures. To combat the effects of channel noise, a novel joint source-channel coding scheme is employed, which uses punctured convolutional channel codes and trellis-coded quantization. At the receiver, the image is reconstructed by proper assembly of the data received from each channel. In case of channel failure, the missing coefficients are estimated from the available data with the use of a novel post processing scheme. For operation over four noisy channels with various bit error probabilities, we investigate the quantitative and subjective performance with and without channel failures.
Morphological Reversible Contour Representation

In this paper, a novel morphological reversible contour representation of discrete binary images is proposed. A binary image is represented by a set of nonoverlapping multilevel contours and a residual image. In this proposed representation, the total number of pixels representing an image is far less than the total number of pixels obtained by the seed-based morphological contour-skeleton lossless representation. The proposed contour representation is simple, unique, and general without restrictions on the binary image to be represented. The resulting multicontour image component is robust to noise. An efficient differential chain contour coding scheme is employed to further compress the represented image. The proposed method yields very low bit rates compared to the existing morphological techniques. An automatic filling procedure, which properly fills a proper multicontour image according to its topological structure without need of seed points, is proposed.
Non-wet solder joint detection in processor sockets and BGA assemblies

The existence of non-wet solder joints in PCB sockets can cause boards failures and it's necessary to inspect theses sockets to locate any possible defective joints. 2D or advanced x-ray machines are used to image solder joints in processor sockets and make solder joints visible to be examined by the operator who determines if each individual joint is defective or not. This is a very time consuming process since each processor has an average of 150 images with 30 joints per image. An accurate and efficient non-wet detection method is proposed in this paper. The main components of the proposed method consist of region of interest (ROI) segmentation, feature extraction, reference-free classification, and automatic mapping. The ROI segmentation process is a noise-resilient segmentation method for the joint area. The centroids of the segmented joints (ROIs) are used as feature parameters to detect the suspect joints. The proposed reference-free classification can detect defective joints with high accuracy without the need for training data. An automatic mapping method is used to get the precise label and location of the suspect joint. The accuracy of the proposed method was determined to be 95.8% detection rate with 1.1% false alarm rate based on the examination of 56 sockets (500K joints). In comparison, the detection rates of currently available advanced x-ray tools with multi-dimension capability are in the range of 43% to 75%. The proposed method reduces the operator effort by 90%. The presented system identifies neighboring joints to any missed non-wet joints, which provides an operator with the capability to make 100% detection of all non-wets. The proposed scheme works with a 2D x-ray imaging device, which makes the proposed scheme relatively inexpensive to implement, and is very portable and easy to set up as compared to the available advanced x-ray tools with multi-dimension capability.
Channel-optimized trellis-coded quantization for channels with memory

This paper presents the design of channel-optimized trellis-coded quantization (COTCQ) for channels with memory. The considered channel model is a binary channel with additive Markov noise. The proposed COTCQ stage exploits the channel memory by incorporating the characteristics of the correlated noise in the quantizer design. We investigate the performance of the memory-optimized COTCQ system for different bit error probabilities and noise correlation coefficients. We show that for a first-order Markov channel, COTCQ significantly outperforms channel-optimized scalar quantizer (COSQ), provided that an appropriate trellis is selected.