Pirkko Oittinen

Method for measuring stereo camera depth accuracy based on stereoscopic vision

We present a method to evaluate stereo camera depth accuracy in human centered applications. It enables the comparison between stereo camera depth resolution and human depth resolution. Our method uses a multilevel test target which can be easily assembled and used in various studies. Binocular disparity enables humans to perceive relative depths accurately, making a multilevel test target applicable for evaluating the stereo camera depth accuracy when the accuracy requirements come from stereoscopic vision. The method for measuring stereo camera depth accuracy was validated with a stereo camera built of two SLRs (singlelens reflex). The depth resolution of the SLRs was better than normal stereo acuity at all measured distances ranging from 0.7 m to 5.8 m. The method was used to evaluate the accuracy of a lower quality stereo camera. Two parameters, focal length and baseline, were varied. Focal length had a larger effect on stereo cameras depth accuracy than baseline. The tests showed that normal stereo acuity was achieved only using a tele lens. However, a users depth resolution in a video see-through system differs from direct naked eye viewing. The same test target was used to evaluate this by mixing the levels of the test target randomly and asking users to sort the levels according to their depth. The comparison between stereo camera depth resolution and perceived depth resolution was done by calculating maximum erroneous classification of levels.

Search strategies in multimodal image retrieval

This paper reports on a study on search strategies in multimodal image retrieval. We analyzed the queries and search tactics employed by image journalism professionals and non-professionals in a user test. Transaction log data show that searchers are able to combine up to four query modes into a query. Most queries combined at least two of the modes (text, color, sketch, quality, and category). Task type was shown to affect the choice of which modes to employ. Known item and data search tasks led to queries combining text, color and category modes. Visually cued tasks resulted in searches combining several content-based and textual modes. Conceptual tasks led to a large number of queries by text and category only. User background also significantly affected the types of queries constructed. Professionals used the color mode more whereas non-professionals drew more sketches. Non-professionals were more likely to switch query modes whereas professionals edited the content of their queries. We described the search processes with Markov models and maximal repeating patterns. Common patterns and probable transitions dealt with querying, inspecting result images and saving them into the workspace or iterating queries of the same type. The results indicate a need to support multimodal image query formulation.

Naturalness and interestingness of test images for visual quality evaluation

Balanced and representative test images are needed to study perceived visual quality in various application domains. This study investigates naturalness and interestingness as image quality attributes in the context of test images. Taking a top-down approach we aim to find the dimensions which constitute naturalness and interestingness in test images and the relationship between these high-level quality attributes. We compare existing collections of test images (e.g. Sony sRGB images, ISO 12640 images, Kodak images, Nokia images and test images developed within our group) in an experiment combining quality sorting and structured interviews. Based on the data gathered we analyze the viewer-supplied criteria for naturalness and interestingness across image types, quality levels and judges. This study advances our understanding of subjective image quality criteria and enables the validation of current test images, furthering their development.

Semi-automated magazine layout using content-based image features

We present a system for automating magazine layout process and data on its performance in a user evaluation test. The purpose of the study is to find the feasibility of the key system variables on the end-user. This semi-automatic system is based on content-based image feature algorithms to automate the layout process. The image related automation includes image cropping, overlaying text on top of images, image color palette creation, and image alignment. The algorithms rely on principles of photography and are used here together in the context of graphic design. For example the rule-of-space and leading line concepts can be extended to layout contexts for improved alignment of text and images. The computation is based on automatic analysis, such as face detection, color saliency, and textureness. We used the automation to create a functional prototype, an iPad magazine, using an open HTML5 eBook framework, which relies on CSS3 media queries for the layout adaptation. User experiments (40 participants) were conducted where the system was compared against two commercial iPad magazine systems in terms of overall usability with emphasis on visual aspects. Questionnaires and free commenting was used in a task-based usage scenario. The experiments were video recorded and the user comments were transcribed and coded into attributes. Usability results mixed with qualitative observation are reported. Results show that usability, read- ability and visuality are important to the users. Our system was considered more usable than the other two systems with some of the defining aspects being simplicity in terms of usability, subjective readability and visual clarity.

Content-Based Prediction of Movie Style, Aesthetics, and Affect: Data Set and Baseline Experiments

The affective content of a movie is often considered to be largely determined by its style and aesthetics. Recently, studies have attempted to estimate affective movie content with computational features, but results have been mixed, one of the main reasons being a lack of data on perceptual stylistic and aesthetic attributes of film, which would provide a ground truth for the features. The distinctions between energetic and tense arousal as well as perceived and felt affect are also often neglected. In this study, we present a data set of ratings by 73 viewers of 83 stylistic, aesthetic, and affective attributes for a selection of movie clips containing complete scenes taken from mainstream movies. The affective attributes include the temporal progression of perceived and felt valence and arousal within the clips. The data set is aimed to be used to train algorithms that predict viewer assessments based on low-level computational features. With this data set, we performed a baseline study modeling the relation between a large selection of low-level computational features (i.e., visual, auditory, and temporal) and perceptual stylistic, aesthetic, and affective attributes of movie clips. Two algorithms were compared in a realistic prediction scenario: linear regression and the neural-network-based Extreme Learning Machine (ELM). Felt and perceived affect as well as stylistic attributes were shown to be equally easy to predict, whereas the prediction of aesthetic attributes failed. The performance of the ELM predictor was overall found to be slightly better than the linear regression. A feature selection experiment illustrated that features from all low-level computational modalities, visual, auditory and temporal, contribute to the prediction of the affect assessments. We have made our assessment data and extracted computational features publicly available.

Improving relative depth judgments in augmented reality with auxiliary augmentations

Significant depth judgment errors are common in augmented reality. This study presents a visualization approach for improving relative depth judgments in augmented reality. The approach uses auxiliary augmented objects in addition to the main augmentation to support ordinal and interval depth judgment tasks. The auxiliary augmentations are positioned spatially near real-world objects, and the location of the main augmentation can be deduced based on the relative depth cues between the augmented objects. In the experimental part, the visualization approach was tested in the “X-ray” visualization case with a video see-through system. Two relative depth cues, in addition to motion parallax, were used between graphical objects: relative size and binocular disparity. The results show that the presence of auxiliary objects significantly reduced errors in depth judgment. Errors in judging the ordinal location with respect to a wall (front, at, or behind) and judging depth intervals were reduced. In addition to reduced errors, the presence of auxiliary augmentation increased the confidence in depth judgments, and it was subjectively preferred. The visualization approach did not have an effect on the viewing time.

Stereoscopic depth perception in video see-through augmented reality within action space

Abstract. Depth perception is an important component of many augmented reality applications. It is, however, subject to multiple error sources. In this study, we investigated depth judgments with a stereoscopic video see-through head-mounted display for the purpose of designing depth cueing for systems that operate in an individual’s action space. In the experiment, we studied the use of binocular disparity and relative size to improve relative depth judgments of augmented objects above the ground plane. The relative size cue was created by adding auxiliary augmentations to the scene according to constraints described in the section on the underlying theory. The results showed that binocular disparity and relative size improved depth judgments over the distance range. This indicates that for accurate depth judgments, additional depth cues should be used to facilitate stereoscopic perception within an individual’s action space.

Is there hope for predicting human visual quality experience

One of the most important research goals in media science is a computational model for the human perception of visual quality, that is, how to predict the subjective visual quality experience. This research area has converged to developing new and investigating existing lower-level measurable quantities, physical, visual or computational, which could explain the high level experience. A principal research question, whether the prediction of the visual quality experience based on any lower-level objective measurements is possible at all, has received much less attention. This question is investigated in this study. First, we describe a large psychological experiment where true factors of the human quality experience are pair-wise resolved for dedicatedly selected samples. Second, we describe a ranking measure which reveals the relationship between selected measurable quantities and the human evaluation. Finally, the presented ranking method is used to provide quantitative evidence that visual quality experience can be predicted using lower-level measurable quantities. This result is novel and by simultaneously revealing the underlying lower-level factors it should re-direct the future research towards the true model.

Negativity bias in media multitasking: The effects of negative social media messages on attention to television news broadcasts

Television viewers’ attention is increasingly more often divided between television and “second screens”, for example when viewing television broadcasts and following their related social media discussion on a tablet computer. The attentional costs of such multitasking may vary depending on the ebb and flow of the social media channel, such as its emotional contents. In the present study, we tested the hypothesis that negative social media messages would draw more attention than similar positive messages. Specifically, news broadcasts were presented in isolation and with simultaneous positive or negative Twitter messages on a tablet to 38 participants in a controlled experiment. Recognition memory, gaze tracking, cardiac responses, and self-reports were used as attentional indices. The presence of any tweets on the tablet decreased attention to the news broadcasts. As expected, negative tweets drew longer viewing times and elicited more attention to themselves than positive tweets. Negative tweets did not, however, decrease attention to the news broadcasts. Taken together, the present results demonstrate a negativity bias exists for social media messages in media multitasking; however, this effect does not amplify the overall detrimental effects of media multitasking.

Finding best measurable quantities for predicting human visual quality experience

The literature of visual quality is mainly concentrated on devising new physical, visual, or computational quality features which could indirectly reflect ldquotrue visual qualityrdquo. The problem is that the true visual quality is always a subjective and context sensitive judgement of a single individual or a group of individuals. Therefore, the developed methods are only loosely connected to this ultimate objective, and the existing de facto and official standards have been designed by forming a consensus among experts of a specific field (e.g., in the printing industry). In this study, we describe a large psychological experiment where true factors of the human quality experience are pair-wise resolved for dedicatedly selected samples. Then we describe a ranking measure which reveals the relationship between selected measurable quantities and the human evaluation trial. Finally by using the above framework, we devise the best combinations from a set of well-known measurable quantities. The devised combinations can be considered as optimal when agreement with the human visual quality experience is desired, and therefore, they also reveal completely novel information about measuring visual quality.