Estimation of Scribble Placement for Painting Colorization

Image colorization has been a topic of interest since the mid 70’s and several algorithms have been proposed that given a grayscale image and color scribbles (hints) produce a colorized image. Recently, this approach has been introduced in the field of art conservation and cultural heritage, where B&W photographs of paintings at previous stages have been colorized. However, the questions of what is the minimum number of scribbles necessary and where they should be placed in an image remain unexplored. Here we address this limitation using an iterative algorithm that provides insights as to the relationship between locally vs. globally important scribbles. Given a color image we randomly select scribbles and we attempt to color the grayscale version of the original.We define a scribble contribution measure based on the reconstruction error. We demonstrate our approach using a widely used colorization algorithm and images from a Picasso painting and the peppers test image. We show that areas isolated by thick brushstrokes or areas with high textural variation are locally important but contribute very little to the overall representation accuracy. We also find that for the case of Picasso on average 10% of scribble coverage is enough and that flat areas can be presented by few scribbles. The proposed method can be used verbatim to test any colorization algorithm

Retrieval accuracy of very large DNA-Based databases of digital signals

In this paper a simulation of single query searches in very large DNA-based databases that are capable of storing and retrieving digital signals is presented. Similarly to the digital domain, a signal-to-noise ratio (SNR) measure to assess the performance of theDNA-based retrieval scheme in terms of database size and source statistics is defined. With approximations, it is shown that the SNR of any finite sizeDNA-based database is upper bounded by the SNR of an infinitely large one with the same source distribution. Computer simulations are presented to validate the theoretical outcomes

Rotation-Invariant Restricted Boltzmann Machine Using Shared Gradient Filters

Finding suitable features has been an essential problem in computer vision. We focus on Restricted Boltzmann Machines (RBMs), which, despite their versatility, cannot accommodate transformations that may occur in the scene. As a result, several approaches have been proposed that consider a set of transformations, which are used to either augment the training set or transform the actual learned filters. In this paper, we propose the Explicit Rotation-Invariant Restricted Boltzmann Machine, which exploits prior information coming from the dominant orientation of images. Our model extends the standard RBM, by adding a suitable number of weight matrices, associated with each dominant gradient. We show that our approach is able to learn rotation-invariant features, comparing it with the classic formulation of RBM on the MNIST benchmark dataset. Overall, requiring less hidden units, our method learns compact features, which are robust to rotations.Comment: 8 pages, 3 figures, 1 tabl

In silico estimation of annealing specificity of query searches in DNA databases

We consider DNA implementations of databases for digital signals with retrieval and mining capabilities. Digital signals are encoded in DNA sequences and retrieved through annealing between query DNA primers and data carrying DNA target sequences. The hybridization between query and target can be non-specific containing multiple mismatches thus implementing similarity-based searches. In this paper we examine theoretically and by simulation the efficiency of such a system by estimating the concentrations of query-target duplex formations at equilibrium. A coupled kinetic model is used to estimate the concentrations. We offer a derivation that results in an equation that is guaranteed to have a solution and can be easily and accurately solved computationally with bi-section root-finding methods. Finally, we also provide an approximate solution at dilute query concentrations that results in a closed form expression. This expression is used to improve the speed of the bi-section algorithm and also to find a closed form expression for the specificity ratios

Restoration of the cantilever bowing distortion in Atomic Force Microscopy

Due to the mechanics of the Atomic Force Microscope (AFM), there is a curvature distortion (bowing effect) present in the acquired images. At present, flattening such images requires human intervention to manually segment object data from the background, which is time consuming and highly inaccurate. In this paper, an automated algorithm to flatten lines from AFM images is presented. The proposed method classifies the data into objects and background, and fits convex lines in an iterative fashion. Results on real images from DNA wrapped carbon nanotubes (DNACNTs) and synthetic experiments are presented, demonstrating the effectiveness of the proposed algorithm in increasing the resolution of the surface topography. In addition a link between the flattening problem and MRI inhomogeneity (shading) is given and the proposed method is compared to an entropy based MRI inhomogeniety correction method

DNA as a medium for storing digital signals

Motivated by the storage capacity and efficiency of the DNA molecule in this paper we propose to utilize DNA molecules to store digital signals. We show that hybridization of DNA molecules can be used as a similarity criterion for retrieving digital signals encoded and stored in a DNA database. Since retrieval is achieved through hybridization of query and data carrying DNA molecules, we present a mathematical model to estimate hybridization efficiency (also known as selectivity annealing). We show that selectivity annealing is inversely proportional to the mean squared error (MSE) of the encoded signal values. In addition, we show that the concentration of the molecules plays the same role as the decision threshold employed in digital signal matching algorithms. Finally, similarly to the digital domain, we define a DNA signal-to-noise ratio (SNR) measure to assess the performance of the DNA-based retrieval scheme. Simulations are presented to validate our arguments

Tracking-Optimized Quantization for H.264 Compression in Transportation Video Surveillance Applications

We propose a tracking-aware system that removes video components of low tracking interest and optimizes the quantization during compression of frequency coefficients, particularly those that most influence trackers, significantly reducing bitrate while maintaining comparable tracking accuracy. We utilize tracking accuracy as our compression criterion in lieu of mean squared error metrics. The process of optimizing quantization tables suitable for automated tracking can be executed online or offline. The online implementation initializes the encoding procedure for a specific scene, but introduces delay. On the other hand, the offline procedure produces globally optimum quantization tables where the optimization occurs for a collection of video sequences. Our proposed system is designed with low processing power and memory requirements in mind, and as such can be deployed on remote nodes. Using H.264/AVC video coding and a commonly used state-of-the-art tracker we show that while maintaining comparable tracking accuracy our system allows for over 50% bitrate savings on top of existing savings from previous work

Feeding the world one open access plant phenotype image at a time

Plant phenotyping is the identification of effects on plant structure and function (the phenotype) resulting from genotypic differences (i.e., differences in the genetic code) and the environmental conditions a plant has been exposed to. Knowledge of plant phenotypes is a key ingredient of the knowledge-based bioeconomy, which not only literally helps to feed the world, but is also essential for feed, fibre and fuel production. While collection of phenotypic traits was previously manual, non-invasive, image-based methods are now increasingly utilized in plant phenotyping and the resulting images need to be analysed in a high throughput, robust, accurate, and reliable manner. Unfortunately, without automated and accurate computer vision to extract the phenotypes, a bottleneck is formed, hampering our understanding of plant biology. As we started our involvement in this field (about 10 years ago) we quickly realized that data are not openly available and this could hamper research, and algorithm development. With the motivation of getting such data for a computer vision challenge (as part of a series of computer vision workshops on solving these problems) we established in 2014 a collection of benchmark datasets of raw and annotated top-view colour images of rosette plants. In 2014, we published a technical report and a paper describing the data, how they were collected and (partly) annotated. In the paper we describe exemplary use cases and results on some tasks obtained with parts of these data. We hoped with the release of this rigorous dataset collection to invigorate the development of algorithms in the context of plant phenotyping but also provide new interesting datasets for the general computer vision community to experiment on. In this short talk I will describe the dataset, where we succeeded and where we believe we could do better. I will also describe certain challenges that arose in the process. A video of this presentation can be viewed at https://media.ed.ac.uk/media/0_kl7hvon