nils-herrmann/clean_scirepeval huggingface dataset:
A method for recognising household tools using the wavelet transform

A method to recognise household tools based on the wavelet transform is presented. Object contours are represented at different resolution levels based on the wavelet transform zero-crossing representation. A dissimilarity function is developed and used to recognise an object by comparing its representation with those in the data base. The method which is translation, rotation and zoom invariant, has been tested on real images under noise-free and noisy conditions.
An Update-Describe Approach for Human Action Recognition in Surveillance Video

In this paper, an approach for human action recognition is presented based on adaptive bag-of-words features. Bag-of-words techniques employ a codebook to describe a human action. For successful recognition, most action recognition systems currently require the optimal codebook size to be determined, as well as all instances of human actions to be available for computing the features. These requirements are difficult to satisfy in real life situations. An update - describe method for addressing these problems is proposed. Initially, interest point patches are extracted from action clips. Then, in the update step these patches are clustered using the Clustream algorithm. Each cluster centre corresponds to a visual word. A histogram of these visual words representing an action is constructed in the describe step. A chi-squared distance-based classifier is utilised for recognising actions. The proposed approach is implemented on benchmark KTH and Weizmann datasets.
Scale invariant feature matching with wide angle images

Numerous scale-invariant feature matching algorithms using scale-space analysis have been proposed for use with perspective cameras, where scale-space is defined as convolution with a Gaussian. The contribution of this work is a method suitable for use with wide angle cameras. Given an input image, we map it to the unit sphere and obtain scale-space images by convolution with the solution of the spherical diffusion equation on the sphere which we implement in the spherical Fourier domain. Using such an approach, the scale-space response of a point in space is independent of its position on the image plane for a camera subject to pure rotation. Scale-invariant features are then found as local extrema in scale-space. Given this set of scale-invariant features, we then generate feature descriptors by considering a circular support region defined on the sphere whose size is selected relative to the feature scale. We compare our method to a naive implementation of SIFT where the image is treated as perspective, where our results show an improvement in matching performance.
Insights Into Students' Conceptual Understanding Using Textual Analysis: A Case Study in Signal Processing

Concept inventory tests are one method to evaluate conceptual understanding and identify possible misconceptions. The multiple-choice question format, offering a choice between a correct selection and common misconceptions, can provide an assessment of students' conceptual understanding in various dimensions. Misconceptions of some engineering concepts exist due to a lack of mental frameworks, or schemas, for these types of concepts or conceptual areas. This study incorporated an open textual response component in a multiple-choice concept inventory test to capture written explanations of students' selections. The study's goal was to identify, through text analysis of student responses, the types and categorizations of concepts in these explanations that had not been uncovered by the distractor selections. The analysis of the textual explanations of a subset of the discrete-time signals and systems concept inventory questions revealed that students have difficulty conceptually explaining several dimensions of signal processing. This contributed to their inability to provide a clear explanation of the underlying concepts, such as mathematical concepts. The methods used in this study evaluate students' understanding of signals and systems concepts through their ability to express understanding in written text. This may present a bias for students with strong written communication skills. This study presents a framework for extracting and identifying the types of concepts students use to express their reasoning when answering conceptual questions.
Personal identification using images of the human palm

A prototype system for human identification using images of the palm is introduced. An image capture platform is constructed such that variation of the camera-to-palm distance is minimised to eliminate the need to compensate for scaling transformation. Appropriate lighting conditions are considered in order to enhance the palm features during image capture. The Hough transform is used to detect the palm features as approximated straight lines. Translation and rotation invariance are achieved by using the edge of the palm as a reference for all feature measurements.
Using textual analysis with concept inventories to identify root causes of misconceptions

Engineers must have deep and accurate conceptual understanding of their field and Concept inventories (CIs) are one method of assessing conceptual understanding and providing formative feedback. Current CI tests use Multiple Choice Questions (MCQ) to identify misconceptions and have undergone reliability and validity testing to assess conceptual understanding. However, they do not readily provide the diagnostic information about students' reasoning and therefore do not effectively point to specific actions that can be taken to improve student learning. We piloted the textual component of our diagnostic CI on electrical engineering students using items from the signals and systems CI. We then analysed the textual responses using automated lexical analysis software to test the effectiveness of these types of software and interviewed the students regarding their experience using the textual component. Results from the automated text analysis revealed that students held both incorrect and correct ideas for certain conceptual areas and provided indications of student misconceptions. User feedback also revealed that the inclusion of the textual component is helpful to students in assessing and reflecting on their own understanding.
Utilizing Least Significant Bit-Planes of RONI Pixels for Medical Image Watermarking

We propose a computationally efficient image border pixel based watermark embedding scheme for medical images. We considered the border pixels of a medical image as RONI (region of non-interest), since those pixels have no or little interest to doctors and medical professionals irrespective of the image modalities. Although RONI is used for embedding, our proposed scheme still keeps distortion at a minimum level in the embedding region using the optimum number of least significant bit-planes for the border pixels. All these not only ensure that a watermarked image is safe for diagnosis, but also help minimize the legal and ethical concerns of altering all pixels of medical images in any manner (e.g, reversible or irreversible). The proposed scheme avoids the need for RONI segmentation, which incurs capacity and computational overheads. The performance of the proposed scheme has been compared with a relevant scheme in terms of embedding capacity, image perceptual quality (measured by SSIM and PSNR), and computational efficiency. Our experimental results show that the proposed scheme is computationally efficient, offers an image-content-independent embedding capacity, and maintains a good image quality of RONI while keeping all other pixels in the image untouched.
Static image simulation of electronic visual prostheses

The development of electronic visual prostheses (artificial human vision/bionic eye systems) is steadily progressing due to the combined efforts of several international research teams. In order to anticipate informative image processing strategies that could be used in these prostheses systems, we have undertaken psychophysical testing using low quality images to simulate visual representation associated with electronic visual prostheses. Our objective is to investigate how much information and what types of information are needed to recognise or perceive a scene, when most of the original scene data is lost. This paper describes results from testing of 174 normally sighted subjects who viewed a set of low quality (low spatial resolution and low grey-scale) static images. These experiments have identified informative image processing operations which can improve understanding of picture content.
Scale-Invariant Features on the Sphere

This paper considers an application of scale-invariant feature detection using scale-space analysis suitable for use with wide field of view cameras. Rather than obtain scale- space images via convolution with the Gaussian function on the image plane, we map the image to the sphere and obtain scale-space images as the solution to the heat (diffusion) equation on the sphere which is implemented in the frequency domain using spherical harmonics. The percentage correlation of scale-invariant features that may be matched between any two wide-angle images subject to change in camera pose is then compared using each of these methods. We also present a means by which the required sampling bandwidth may be determined and propose a suitable anti-aliasing filter which may be used when this bandwidth exceeds the maximum permissible due to computational requirements. The results show improved performance using scale-space images obtained as the solution of the diffusion equation on the sphere, with additional improvements observed using the anti-aliasing filter.
Texture for script identification

The problem of determining the script and language of a document image has a number of important applications in the field of document analysis, such as indexing and sorting of large collections of such images, or as a precursor to optical character recognition (OCR). In this paper, we investigate the use of texture as a tool for determining the script of a document image, based on the observation that text has a distinct visual texture. An experimental evaluation of a number of commonly used texture features is conducted on a newly created script database, providing a qualitative measure of which features are most appropriate for this task. Strategies for improving classification results in situations with limited training data and multiple font types are also proposed.
Map Building Using Cheap Digital Cameras

Cheap digital cameras are readily available. They can be mounted on robots and used to build maps of the surrounding environment. However, these cameras suffer from several drawbacks such as a narrow field of view, low resolution and limited range due to perspective. These limitations can cause traditional approaches to Simultaneous Localization and Mapping to fail due to insufficient information content in the visual sensor data. This paper discusses these issues and presents a solution for indoor environments.
Adaptive unsupervised learning of human actions

Automatic detection of suspicious activities in CCTV camera feeds is crucial to the success of video surveillance systems. Such a capability can help transform the dumb CCTV cameras into smart surveillance tools for fighting crime and terror. Learning and classification of basic human actions is a precursor to detecting suspicious activities. Most of the current approaches rely on a non-realistic assumption that a complete dataset of normal human actions is available. 
 
This paper presents a different approach to deal with the problem of understanding human actions in video when no prior information is available. This is achieved by working with an incomplete dataset of basic actions which are continuously updated. Initially, all video segments are represented by Bags-Of-Words (BOW) method using only Term Frequency-Inverse Document Frequency (TF-IDF) features. Then, a data-stream clustering algorithm is applied for updating the system's knowledge from the incoming video feeds. Finally, all the actions are classified into different sets. Experiments and comparisons are conducted on the well known Weizmann and KTH datasets to show the efficacy of the proposed approach.
Recognition of space curves based on the dyadic wavelet transform

An algorithm for recognising space curves is introduced. The algorithm includes two stages : construction of space curve representation and a matching procedure. The representation is based on a dyadic wavelet transform. A dissimilarity function is defined and used in the matching procedure. Experimental results show that for 3D objects that can be represented by space curves, this algorithm is robust and efficient in extracting and matching object information.<<ETX>>
Directed Exploration Using a Modified Distance Transform

Mobile robots operating in unknown environments need to build maps. To do so they must have an exploration algorithm to plan a path. This algorithm should guarantee that the whole of the environment, or at least some designated area, will be mapped. The path should also be optimal in some sense and not simply a "random walk" which is clearly inefficient. When multiple robots are involved, the algorithm also needs to take advantage of the fact that the robots can share the task. In this paper we discuss a modification to the well-known distance transform that satisfies these requirements.
An image processing approach for estimating the number of live prawn larvae in water

We address the problem of accurate estimation of prawn larvae numbers in small water-filled containers using image processing techniques. Images of the containers are captured from sampled video signals of a camera suspended over the containers. The images are preprocessed for noise removal and enhancement. The resulting images are then processed to estimate the number of prawn larvae with an acceptable accuracy. Our preliminary results show that estimates of prawn larvae numbers with accuracy of between 90-95% are achievable under controlled conditions. Some issues of system development and practical implementation are discussed.
Attitude Estimation for a Fixed-Wing Aircraft Using Horizon Detection and Optical Flow

We develop a method for estimating the flight critical parameters of pitch angle, roll angle and the three body rates using horizon detection and optical flow. We achieve this through the use of an image processing front-end to detect candidate horizon lines through the use of morphological image processing and the Hough transform. The optical flow of the image for each candidate line is calculated, and using these measurements, we are able to estimate the body rates of the aircraft. Using an Extended Kalman Filter (EFK), the candidate horizon lines are propagated and tracked through successive image frames, with statistically unlikely horizon candidates eliminated.  qualitativly describing the performance of the image processing front-end on real datasets are presented, followed by an analysis of the improvement when utilising the motion model of the vehicle.
Investigation of Fish-Eye Lenses for Small-UAV Aerial Photography

Aerial photography obtained by unmanned aerial vehicles (UAVs) is a rising market for their civil application. Small UAVs are believed to close gaps in niche markets, such as acquiring airborne image data for remote sensing purposes. Small UAVs can fly at low altitudes, in dangerous environments, and over long periods of time. However, their small lightweight construction leads to new problems, such as higher agility and more susceptibility to turbulence, which has a big impact on the quality of the data and their suitability for aerial photography. This paper investigates the use of fish-eye lenses to overcome field-of-view (FOV) issues for highly agile UAV platforms susceptible to turbulence. The fish-eye lens has the benefit of a large observation area (large FOV) and does not add additional weight to the aircraft, such as traditional mechanical stabilizing systems. We present the implementation of a fish-eye lens for aerial photography and mapping purposes, with potential use in remote sensing applications. We describe a detailed investigation from the fish-eye lens distortion to the registering of the images. Results of the process are presented using low-quality sensors typically found on small UAVs. The system was flown on a midsize platform (a more stable Cessna aircraft) and also on ARCAA's small (<10 kg) UAV platform. The effectiveness of the approach is compared for the two sized platforms.
Recursive two-dimensional median filtering algorithms for fast image root extraction

The authors develop and evaluate two new two-dimensional fast recursive median filtering algorithms, for image root extraction. The first recursive algorithm, which is called the fast binary root algorithm, converges to a root of a binary image in one pass including a number of local rescans. The second is referred to as the fast multilevel root algorithm. It converges to a root of a multilevel image in two passes, the second of which includes a number of local rescans. The number of ordering positions required by the author's algorithms is shown to be significantly less than those required by the standard and regular recursive median filters.
Visual perception of low quality images

There are several new applications where perception is required from low quality images. One such application are electronic visual prostheses, or "bionic eyes". These artificial vision systems involve electrical stimulation of nerve cells in the human visual system via implanted electrodes. In this paper we present results of our subjective tests based on simulating what might be seen by users of low quality vision systems. 225 normally sighted subjects viewed a set of low quality (low spatial resolution and low grey-scale) static images. We wished to quantify intelligibility/recognition for low quality images. Results from this testing form part of an image quality model to assess the usefulness of low quality images.
Wavelet-based affine invariant representation: a tool for recognizing planar objects in 3D space

A technique is developed to construct a representation of planar objects undergoing a general affine transformation. The representation can be used to describe planar or nearly planar objects in a three-dimensional space, observed by a camera under arbitrary orientations. The technique is based upon object contours, parameterized by an affine invariant parameter and the dyadic wavelet transform. The role of the wavelet transform is the extraction of multiresolution affine invariant features from the affine invariant contour representation. A dissimilarity function is also developed and used to distinguish among different object representations. This function makes use of the extrema on the representations, thus making its computation very efficient. A study of the effect of using different wavelet functions and their order or vanishing moments is also carried out. Experimental results show that the performance of the proposed representation is better than that of other existing methods, particularly when objects are heavily corrupted with noise.
Vibration Compensation for Fisheye Lenses in UAV Applications

Low-cost aerial vision systems need to face the challenges of using low quality products to perform aerial photography. Such systems are widely used in remote controlled aircrafts and unmanned aerial vehicles (UAVs) to collect aerial imagery and are used for image acquisition, terrain mapping or remote sensing. A one-pixel shift in a 0.8 mega pixel resolution image captured from a UAV operating at 1000ft will correspond to about 2.5m measurement error on the ground. In our case, a vibrating fisheye lens moving relative to the camera added new uncertainties in the collected images and required compensation. This paper presents a vibration compensation approach using a modified Hough Transform utilizing the circle shaped image provided by a fisheye lens. We define the fisheye circle boundary by using a Canny edge detector. Our vibration compensation was tested using our collected aerial images with enhanced performance in more than 80% of the cases.
Recognition of 2D object contours using the wavelet transform zero-crossing representation

A new algorithm to recognize a two-dimensional object of arbitrary shape is presented. The object boundary is first represented by a one-dimensional signal. This signal is then used to build the wavelet transform zero-crossing representation of the object. The algorithm is invariant to translation, rotation and scaling. Experimental results show that, compared with the use of Fourier descriptors, our algorithm gives more stable and accurate results.