Youth Expression with Video Surveillance Technology

This qualitative study examines five young Afro-Franco Caribbean males in the Diaspora and their experiences with systems of technology as a tool of oppression and liberation. The study utilized interpretive biography and participatory video research to examine the issues of identity, power/control, surveillance technology, love and freedom. The study made use of a number of data collection methods including interviews, round table discussions, and personal narratives. A hermeneutic theoretical framework is employed to develop an objective view of the problems facing Afro-Franco Caribbean males in the schools and community. The purpose of the study is to provide an environment and new media technology that Afro-Franco Caribbean males can use to engage and discuss their views on issues mentioned above and to ultimately develop a video project to share with the community. Moreover, the study sought to examine an epistemological approach (Creolization) that young black males, particularly Afro-Franco-Caribbean males, might use to communicate, document, and share their everyday experiences in the Diaspora. The findings in the study reveal that the participants are experiencing: (a) a lack of community involvement in the urban space they currently reside, (b) frustration with the perspective of their home country, Haiti, that is commonly shown in mainstream media, and (c) ridicule, shame, and violence in the spaces (school and community) that should be safe. The study provides the community (both local and scholarly) with an opportunity to hear the voices and concerns of youth in the urban space. In addition the study suggests a need for schools to create a critical pedagogical curriculum in which power can be democratically shared.

Identifying player profiles in video games

Tämä diplomityö tarkastelee pelaajatyyppien ja pelaajamotivaatioiden tunnistamista videopeleissä. Aiempi tutkimus tuntee monia pelaajatyyppien malleja, mutta niitä ei ole liiemmin sovellettu käytäntöön peleissä. Tässä työssä suoritetaan systemaattinen kirjallisuuskartoitus erilaisista pelaajatyyppien malleista, jonka pohjalta esitetään useita pelaajien luokittelutapoja. Lisäksi toteutetaan tapaustutkimus, jossa kirjallisuuden pohjalta valitaan pelaajien luokittelumalli ja testataan mallia käytännössä tunnistamalla pelaajatyyppejä data-analytiikan avulla reaaliaikaisessa strategiapelissä.

Deep convolutional neural networks for semantic video object segmentation

In this thesis, we propose to infer pixel-level labelling in video by utilising only object category information, exploiting the intrinsic structure of video data. Our motivation is the observation that image-level labels are much more easily to be acquired than pixel-level labels, and it is natural to find a link between the image level recognition and pixel level classification in video data, which would transfer learned recognition models from one domain to the other one. To this end, this thesis proposes two domain adaptation approaches to adapt the deep convolutional neural network (CNN) image recognition model trained from labelled image data to the target domain exploiting both semantic evidence learned from CNN, and the intrinsic structures of unlabelled video data. Our proposed approaches explicitly model and compensate for the domain adaptation from the source domain to the target domain which in turn underpins a robust semantic object segmentation method for natural videos. We demonstrate the superior performance of our methods by presenting extensive evaluations on challenging datasets comparing with the state-of-the-art methods.

La multimedia y sus aplicaciones: elaboración de un video promocional para la carrera de ingeniería de sistemas

¿Por qué la multimedia? Tradicionalmente, la manera de presentar la información en los computadores ha sido la de los reportes y pantallas, en las que aparecen texto, números y ocasionalmente gráficos. A pesar de que estos medios son efectivos para cierto tipo de comunicaciones, su consulta causa normalmente tedio a las personas, y una información valiosa puede perderse por una manera poco efectiva de comunicarla.

Video lectures and online activities to engage students in a flipped classroom

In recent years there have been several proposals for alternative pedagogical practices. Most of these proposals are based in the, so called, “active learning”, in opposition to the common “passive learning”, which is centered on transmission of information inside classrooms as well as recognized as teacher-centered procedure. In an active learning pedagogical structure, students have a more participative role in the overall learning/teaching process, being encouraged to face new learning challenges like, for instance, solving problems and developing projects, in an autonomous approach trying to make them, consequently, able to build their own knowledge. The flipped or “inverted” classroom is one of these active learning pedagogical methodologies that emphasizes a learner-centered instruction. According to this approach, the first contact that students have with the content on a particular curriculum subject is not transmitted by the lecturer in the classroom, this teaching strategy requires students to assess and analyze the specific subject before attending to class, therefore the informational component from the lecture is the homework, and class time is dedicated to exercises and assignments, always with support from the instructor, who acts as a facilitator, helping students when needed and offering supplementary explanation as required. The main objective of this paper is to discuss and explore how the use of different types of instructional videos and online activities may be implemented in the flipped classroom procedure (as means of incorporating new content and teaching new competencies) and to describe students’ perceptions of this approach within a course in a Higher Education Institution (HEI), presenting some positive and negative features of this pedagogical practice.

Olkapään alueen makroskooppinen anatomia – video-opetusmateriaali anatomian opiskeluun

Anatomian opiskelu perustuu edelleen pitkälti kirjojen piirrettyihin kuviin. Opinnäytetyöni on tarkoitettu täydentämään kirjojen sisältöä ja tukemaan anatomian opiskelua. Tarkoituksena oli tuottaa havainnollinen videomateriaali olkapään alueesta käytettäväksi Turun yliopiston lääketieteellisen tiedekunnan anatomian kursseilla sekä itseopiskelumateriaalina. Videolla keskitytään kahteen erilliseen isoon kokonaisuuteen: kiertäjäkalvosimeen sekä lapaluuhun ja siihen kiinnittyviin lihaksiin. Suomenkielisen opetusvideon avulla opiskelijat voivat ymmärtää paremmin olkapään kolmiulotteista anatomiaa ja hahmottaa rakenteiden välisiä suhteita oikeassa kehossa. Ennen videon kuvaamista kertasin olkapään alueen anatomian, suunnittelin videon sisällön sekä harjoittelin preparointitekniikoita. Preparointia harjoiteltiin yhdellä formaliinivainajalla ja yhdellä tuorevainajalla. Avaussalityöskentelyssä tein yhteistyötä neljän muun opiskelijan kanssa. Kuvasimme kukin omien opinnäytetöidemme aiheet samasta tuorevainajasta yhden viikonlopun aikana 19.–22.12.2014. Video kuvattiin kokonaisuudessaan Turun yliopiston Medisiina- laitosrakennuksen anatomian opetusavaussalissa. Kuvaamisessa käytettiin Kliinisten taitojen oppimiskeskuksen Portin kameraa ja kuvausvälineistöä. Kuvausmateriaali muokattiin lopulliseksi videoksi käyttämällä Adobe Premiere Pro CC 2014 -videonmuokkausohjelmaa. Jälkikäteen videoon lisättiin ääniraita sekä anatomian käsitteitä selventämään videon kulkua. Lopullinen opetusvideo on noin 12 minuuttia kestävä tiivis kokonaisuus olkapään alueen anatomiasta. Se koostuu kolmesta osasta. Ensimmäinen osa on suurin kokonaisuus, jossa käsitellään lapaluun merkittävimmät luiset rakenteet sekä lapaluuhun kiinnittyvät lihakset erikseen edestä ja takaa. Toisessa osassa keskitytään kliinisesti merkittävään olkapään rakenteeseen, kiertäjäkalvosimeen, josta esitellään tarkasti kaikki neljä lihasta ja niiden sijainnit kuvattuna eri suunnista ja eri etäisyyksiltä. Videon lopuksi käydään läpi vielä olkapään kymmenen eri liikettä, jotka esitetään videolla.

Efficient Image and Video Representations for Retrieval

Image (Video) retrieval is an interesting problem of retrieving images (videos) similar to the query. Images (Videos) are represented in an input (feature) space and similar images (videos) are obtained by finding nearest neighbors in the input representation space. Numerous input representations both in real valued and binary space have been proposed for conducting faster retrieval. In this thesis, we present techniques that obtain improved input representations for retrieval in both supervised and unsupervised settings for images and videos. Supervised retrieval is a well known problem of retrieving same class images of the query. We address the practical aspects of achieving faster retrieval with binary codes as input representations for the supervised setting in the first part, where binary codes are used as addresses into hash tables. In practice, using binary codes as addresses does not guarantee fast retrieval, as similar images are not mapped to the same binary code (address). We address this problem by presenting an efficient supervised hashing (binary encoding) method that aims to explicitly map all the images of the same class ideally to a unique binary code. We refer to the binary codes of the images as `Semantic Binary Codes' and the unique code for all same class images as `Class Binary Code'. We also propose a new class­ based Hamming metric that dramatically reduces the retrieval times for larger databases, where only hamming distance is computed to the class binary codes. We also propose a Deep semantic binary code model, by replacing the output layer of a popular convolutional Neural Network (AlexNet) with the class binary codes and show that the hashing functions learned in this way outperforms the state­ of ­the art, and at the same time provide fast retrieval times. In the second part, we also address the problem of supervised retrieval by taking into account the relationship between classes. For a given query image, we want to retrieve images that preserve the relative order i.e. we want to retrieve all same class images first and then, the related classes images before different class images. We learn such relationship aware binary codes by minimizing the similarity between inner product of the binary codes and the similarity between the classes. We calculate the similarity between classes using output embedding vectors, which are vector representations of classes. Our method deviates from the other supervised binary encoding schemes as it is the first to use output embeddings for learning hashing functions. We also introduce new performance metrics that take into account the related class retrieval results and show significant gains over the state­ of­ the art. High Dimensional descriptors like Fisher Vectors or Vector of Locally Aggregated Descriptors have shown to improve the performance of many computer vision applications including retrieval. In the third part, we will discuss an unsupervised technique for compressing high dimensional vectors into high dimensional binary codes, to reduce storage complexity. In this approach, we deviate from adopting traditional hyperplane hashing functions and instead learn hyperspherical hashing functions. The proposed method overcomes the computational challenges of directly applying the spherical hashing algorithm that is intractable for compressing high dimensional vectors. A practical hierarchical model that utilizes divide and conquer techniques using the Random Select and Adjust (RSA) procedure to compress such high dimensional vectors is presented. We show that our proposed high dimensional binary codes outperform the binary codes obtained using traditional hyperplane methods for higher compression ratios. In the last part of the thesis, we propose a retrieval based solution to the Zero shot event classification problem - a setting where no training videos are available for the event. To do this, we learn a generic set of concept detectors and represent both videos and query events in the concept space. We then compute similarity between the query event and the video in the concept space and videos similar to the query event are classified as the videos belonging to the event. We show that we significantly boost the performance using concept features from other modalities.

Video documental biográfico de Eliécer Cárdenas Espinoza y su labor como editorialista de diario El Tiempo: Técnicas y Métodos periodísticos audiovisuales

Presentar en un Video Documental Biográfico, la trayectoria periodística de Eliécer Cárdenas Espinoza como editorialista y articulista en Diario el Tiempo. Además se complementara esta investigación no sólo a través de su lado profesional, también como el hombre de familia y amigo. De igual manera, se podrá visualizar el papel que cumple como Gestor Cultural, Cronista de la ciudad y literato, en el que nos habla de su principal obra Polvo y Ceniza. El video también pone énfasis en la labor que cumple el periodista, el papel de las universidades en la formación de los futuros comunicadores, las nuevas tecnologías que invaden el campo de la comunicación y el futuro del periodismo en las sociedades actuales. El proyecto cuenta con la intervención de docentes y periodistas especializados en el campo de la comunicación, los mismos que aportaron con valiosos testimonios sobre la trayectoria de nuestro personaje y el componente actual en la información. El objetivo de este proyecto es dejar un registro audiovisual académico realizado a base de técnicas y métodos periodísticos muy bien aplicados.

Active Foreground Region Extraction and Tracking for Sports Video Annotation

Automatic video segmentation plays a vital role in sports videos annotation. This paper presents a fully automatic and computationally efficient algorithm for analysis of sports videos. Various methods of automatic shot boundary detection have been proposed to perform automatic video segmentation. These investigations mainly concentrate on detecting fades and dissolves for fast processing of the entire video scene without providing any additional feedback on object relativity within the shots. The goal of the proposed method is to identify regions that perform certain activities in a scene. The model uses some low-level feature video processing algorithms to extract the shot boundaries from a video scene and to identify dominant colours within these boundaries. An object classification method is used for clustering the seed distributions of the dominant colours to homogeneous regions. Using a simple tracking method a classification of these regions to active or static is performed. The efficiency of the proposed framework is demonstrated over a standard video benchmark with numerous types of sport events and the experimental results show that our algorithm can be used with high accuracy for automatic annotation of active regions for sport videos.

Football Video Annotation Based on Player Motion Recognition Using Enhanced Entropy

This paper presents a semi-parametric Algorithm for parsing football video structures. The approach works on a two interleaved based process that closely collaborate towards a common goal. The core part of the proposed method focus perform a fast automatic football video annotation by looking at the enhance entropy variance within a series of shot frames. The entropy is extracted on the Hue parameter from the HSV color system, not as a global feature but in spatial domain to identify regions within a shot that will characterize a certain activity within the shot period. The second part of the algorithm works towards the identification of dominant color regions that could represent players and playfield for further activity recognition. Experimental Results shows that the proposed football video segmentation algorithm performs with high accuracy.

Video - Guía de servicios actualizados e información general dirigido a usuarios de la Clínica de Optometría, Universidad de la Salle

Bogotá (Colombia): Universidad de la Salle. Programa de Optometría

International workshop on "Different survey methods for coral reef fish, including methods based on underwater video"

In September 2013, staff from the University of the South Pacific (USP) Honiara campus, the Secretariat of the Pacific Community (SPC) and IFREMER (UR LEADNC, AMBIO project) in New Caledonia, and the French Institute for Pacific Coral Reefs (IRCP) in Moorea, French Polynesia, co-facilitated a workshop entitled “Different survey methods of coral reef fish, including the methods based on underwater video”. The workshop was attended by students from USP, NGO and fisheries officers. They were trained to several underwater visual census techniques and to the STAVIRO video-based technique, including both field work and data analysis.

A Phylogenetic Classification of the Video-Game Industry’s Business Model Ecosystem

Part 8: Business Strategies Alignment