We have witnessed great interest and a wealth of promise in content-based image retrieval as an emerging technology. While the last decade laid foundation to such promise, it also paved the way for a large number of new techniques and systems, got many new people involved, and triggered stronger association of weakly related fields. In this article, we survey almost 300 key theoretical and empirical contributions in the current decade related to image retrieval and automatic image annotation, and in the process discuss the spawning of related subfields. We also discuss significant challenges involved in the adaptation of existing image retrieval techniques to build systems that can be useful in the real world. In retrospect of what has been achieved so far, we also conjecture what the future may hold for image retrieval research.

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Image retrieval: Ideas, influences, and trends of the new age

IEEE Transactions on Pattern Analysis and Machine Intelligence

Computer and Robot Vision Vol. 1, by R.M. Haralick and Linda G. Shapiro, Addison-Wesley, 1992, ISBN 0-201-10887-1.

Computer and Robot Vision

While research on articulated human motion and pose estimation has progressed rapidly in the last few years, there has been no systematic quantitative evaluation of competing methods to establish the current state of the art. We present data obtained using a hardware system that is able to capture synchronized video and ground-truth 3D motion. The resulting HumanEva datasets contain multiple subjects performing a set of predefined actions with a number of repetitions. On the order of 40,000 frames of synchronized motion capture and multi-view video (resulting in over one quarter million image frames in total) were collected at 60 Hz with an additional 37,000 time instants of pure motion capture data. A standard set of error measures is defined for evaluating both 2D and 3D pose estimation and tracking algorithms. We also describe a baseline algorithm for 3D articulated tracking that uses a relatively standard Bayesian framework with optimization in the form of Sequential Importance Resampling and Annealed Particle Filtering. In the context of this baseline algorithm we explore a variety of likelihood functions, prior models of human motion and the effects of algorithm parameters. Our experiments suggest that image observation models and motion priors play important roles in performance, and that in a multi-view laboratory environment, where initialization is available, Bayesian filtering tends to perform well. The datasets and the software are made available to the research community. This infrastructure will support the development of new articulated motion and pose estimation algorithms, will provide a baseline for the evaluation and comparison of new methods, and will help establish the current state of the art in human pose estimation and tracking.

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HumanEva: Synchronized Video and Motion Capture Dataset and Baseline Algorithm for Evaluation of Articulated Human Motion

In this paper the main problems and the available solutions are addressed for the generation of 3D models from terrestrial images. Close range photogrammetry has dealt for many years with manual or automatic image measurements for precise 3D modelling. Nowadays 3D scanners are also becoming a standard source for input data in many application areas, but image-based modelling still remains the most complete, economical, portable, flexible and widely used approach. In this paper the full pipeline is presented for 3D modelling from terrestrial image data, considering the different approaches and analysing all the steps involved.

/pdf/image-based-3d-modelling-a-review-5msqumyd5o.pdf

Image-based 3d modelling: a review

This paper presents a method for the recognition of Glagolitic characters in degraded historical documents. The Glagolitic character recognition is based on Dense SIFT for which image restoration is proposed as a pre-processing step in order to suppress background noise in degraded documents. Two different methods for image restoration are used which are Total Variation regularization and a new restoration method. Each method performs robustly against background noise while preserving character edges and strokes in the documents defected by stain, bleed through, and faded out ink. The experimental results achieved on three datasets show that by using image restoration as a pre-processing step to Dense SIFT generates better recognition rates for Glagolitic characters in degraded documents.

Recognizing Glagolitic Characters in Degraded Historical Documents

This article investigates the limitations of single image depth prediction (SIDP) under different lighting conditions. Besides that, it also offers a new approach to obtain the ideal condition for SIDP. To satisfy the data requirement, we exploit a photometric stereo dataset consisting of several images of an object under different light properties. In this work, we used a dataset of ancient Roman coins captured under 54 different lighting conditions to illustrate how the approach is affected by them. This dataset emulates many lighting variances with a different state of shading and reflectance common in the natural environment. The ground truth depth data in the dataset was obtained using the stereo photometric method and used as training data. We investigated the capabilities of three different state-of-the-art methods to reconstruct ancient Roman coins with different lighting scenarios. The first investigation compares the performance of a given network using previously trained data to check cross-domains performance. Second, the model is fine-tuned from pre-trained data and trained using 70% of the ancient Roman coin dataset. Both models are tested on the remaining 30% of the data. As evaluation metrics, root mean square error and visual inspection are used. As a result, the methods show different characteristic results based on the lighting condition of the test data. Overall, they perform better at 51° and 71° angles of light, so-called ideal condition afterward. However, they perform worse at 13° and 32° because of the high density of shadows. They also cannot reach the best performance at 82° caused by the reflection that appears on the image. Based on these findings, we propose a new approach to reduce the shadows and reflections on the image using intrinsic image decomposition to achieve a synthetic ideal condition. Based on the results of synthetic images, this approach can enhance the performance of SIDP. For some state-of-the-art methods, it also achieves better results than previous original RGB images.

Investigation of Single Image Depth Prediction Under Different Lighting Conditions: A Case Study of Ancient Roman Coins

Every archaeological excavation must deal with a vast number of ceramic fragments. The documentation, administration and scientific processing of these fragments represent a temporal, personnel, and financial problem. Therefore, we construct a documentation system for archaeological fragments to form the basis for a subsequent semi-automatic classification and reconstruction. The basis for classification and reconstruction is the profile, which is the cross-section of the fragment in the direction of the rotational axis of symmetry. Hence the position of a fragment (orientation) on a vessel is important. To achieve the profile, a 3d-representation of the ob-

Range Image Registration of Rotationally Symmetric Objects

The aim of our work is the development of image analysis tools and methods for the investigation of drawings and drawn drafts in order to investigate the authorship, to identify copies or more general to allow for a comparison of different types of drawings. It was and is common for artists to draw their design as several drafts on paper. These drawings can show how some elements were adjusted until the artist was satisfied with the composition. Therefore it can bring insights into the practice of artists and painting and/or drawing schools. This information is useful for art historians, because it can relate artists to each other. The goal of this paper is to describe a stroke classification algorithm which can recognize the drawing tool based on the shape of the endings of an open stroke. In this context, "open" means that both endings of a stroke are free-standing, uncovered and do not pass into another stroke. These endings are prominent features whose shape carries information about the drawing tool and are therefore used as features to distinguish different drawing tools. Our results show that it is possible to use these endings as input a drawing tool classificator.

Robert Sablatnig

Papers

Recognizing Glagolitic Characters in Degraded Historical Documents

Investigation of Single Image Depth Prediction Under Different Lighting Conditions: A Case Study of Ancient Roman Coins

Range Image Registration of Rotationally Symmetric Objects

Drawing Tool Recognition by Stroke Ending Analysis

Pictorial, Three-dimensional Acquisition of Archaeological Finds as Basis for an Automatic Classification