Holistic, Instance-Level Human Parsing

Object parsing -- the task of decomposing an object into its semantic parts -- has traditionally been formulated as a category-level segmentation problem. Consequently, when there are multiple objects in an image, current methods cannot count the number of objects in the scene, nor can they determine which part belongs to which object. We address this problem by segmenting the parts of objects at an instance-level, such that each pixel in the image is assigned a part label, as well as the identity of the object it belongs to. Moreover, we show how this approach benefits us in obtaining segmentations at coarser granularities as well. Our proposed network is trained end-to-end given detections, and begins with a category-level segmentation module. Thereafter, a differentiable Conditional Random Field, defined over a variable number of instances for every input image, reasons about the identity of each part by associating it with a human detection. In contrast to other approaches, our method can handle the varying number of people in each image and our holistic network produces state-of-the-art results in instance-level part and human segmentation, together with competitive results in category-level part segmentation, all achieved by a single forward-pass through our neural network.Comment: Poster at BMVC 201

Xiaobing Tang. Global space and the nationalist discourse of modernity : the historical thinking of Liang Qichao

This article reviews the book Global Space and the Nationalist Discourse of Modernity: The Historical Thinking of Liang Qichao written by Xiaobing Tang

MORFOLOGI DAN UKURAN LIANG GEREK LARVA PBK Conopomorpha cramerella Snellen (LEPIDOPTERA:GRACILLARIIDAE) PADA BUAH KAKAO

 Tujuan penelitian adalah untuk mempelajari dan mengetahui morfologi dan liang gerek larva instar satu dan instar akhir PBK C. cramerella pada buah kakao di desa Rahmat kecamatan Palolo kabupaten Sigi provinsi Sulawesi Tengah. Hasil penelitian rerata panjang liang gerek larva PBK C. cramerella  instar satu yang baru menetas masuk menggerek buah kakao 0,218±0,005mm, rerata lebar liang gerek larva PBK C. cramerella instar satu 0,148±0,004mm, rerata panjang liang gerek larva instar akhir keluar dari dalam buah kakao 1,481±0,017mm dan rerata lebar liang gerek larva instar akhir 1,239±0,030mm. Rerata panjang telur PBK C cramerella 0,480±0,027mm dan rerata lebar telur 0,288±0,014mm. Rerata panjang larva instar akhir PBK C. cramerella 9,725±0,174 mm, rerata panjang pupa PBK C. cramerella 7,828±0,107mm, rerata panjang imago PBK C. cramerella 6,690±0,125mm dan rerata lebar kapsul kepala larva PBK C. cramerella 0,935±0,040mm. Kata Kunci : C. cramerella, larva instrar akhir, larva instar satu

A Deep Primal-Dual Network for Guided Depth Super-Resolution

In this paper we present a novel method to increase the spatial resolution of depth images. We combine a deep fully convolutional network with a non-local variational method in a deep primal-dual network. The joint network computes a noise-free, high-resolution estimate from a noisy, low-resolution input depth map. Additionally, a high-resolution intensity image is used to guide the reconstruction in the network. By unrolling the optimization steps of a first-order primal-dual algorithm and formulating it as a network, we can train our joint method end-to-end. This not only enables us to learn the weights of the fully convolutional network, but also to optimize all parameters of the variational method and its optimization procedure. The training of such a deep network requires a large dataset for supervision. Therefore, we generate high-quality depth maps and corresponding color images with a physically based renderer. In an exhaustive evaluation we show that our method outperforms the state-of-the-art on multiple benchmarks.Comment: BMVC 201

Deformable Part-based Fully Convolutional Network for Object Detection

Existing region-based object detectors are limited to regions with fixed box geometry to represent objects, even if those are highly non-rectangular. In this paper we introduce DP-FCN, a deep model for object detection which explicitly adapts to shapes of objects with deformable parts. Without additional annotations, it learns to focus on discriminative elements and to align them, and simultaneously brings more invariance for classification and geometric information to refine localization. DP-FCN is composed of three main modules: a Fully Convolutional Network to efficiently maintain spatial resolution, a deformable part-based RoI pooling layer to optimize positions of parts and build invariance, and a deformation-aware localization module explicitly exploiting displacements of parts to improve accuracy of bounding box regression. We experimentally validate our model and show significant gains. DP-FCN achieves state-of-the-art performances of 83.1% and 80.9% on PASCAL VOC 2007 and 2012 with VOC data only.Comment: Accepted to BMVC 2017 (oral

JENIS SHELTER YANG BERBEDA TERHADAP PERTUMBUHAN DAN SINTASAN LOBSTER AIR TAWAR RED CLAW (Cherax quadricarinatus)

Komoditas lobster air tawar mulai masuk Indonesia pada tahun 2000 dan dibudidayakan untuk memenuhi kebutuhan pasar udang hias, pada tahun 2003, untuk memenuhi pasar udang hias mulai beralih trend menjadi salah satu jenis udang konsumsi. Jenis Red claw ini mampu bertahan pada kisaran suhu 23-37°C. Suhu diwilayah Indonesia yang berkisar 27-32°C menyebabkan pertumbuhan lobster air tawar yang lebih baik, sehingga lebih berpotensi untuk dibudidayakan. Sifat kanibal adalah penyebab utama mortalitas pada budidaya dan sering terjadi ketika lobster lain mengalami moulting. Pada habitat aslinya lobster menempati sela-sela bebatuan dan membuat lubang pada dasar perairan yang berlumpur untuk bersembunyi. Dalam budidaya diperlukan lubang atau liang persembunyian buatan dengan tujuan yang sama. Penempatan shelter atau liang perlindungan berguna sebagai tempat persembunyian. Pada awal segmen pembesaran, lobster air tawar memiliki frekuensi moulting yang masih tinggi sehingga perlu adanya shelter sebagai tempat berlindung setelah moulting. Ada beberapa liang perlindungan yang berasal dari bahan yang berbeda misalnya roster dari semen, roster dari tanah liat, tumpukan genteng, daun kelapa yang ditumpuk, serta potongan pipa paralon. Metode yang digunakan adalah metode eksperimen, dengan menggunakan rancangan percobaan yaitu Rancangan Acak Lengkap (RAL) dengan 4 perlakuan, masing-masing perlakuan diulang 3 kali. Setiap wadah ditebar benih lobster ukuran 2 inch dengan kepadatan 10 ekor/wadah. Aplikasi shelter pada setiap perlakuan yaitu: A= eceng gondok, B= pipa paralon, C= batu roster, dan D= botol plastik. Berdasarkan hasil penelitian yang telah dilakukan, dapat disimpulkan bahwa perlakuan shelter yang berbeda hanya menunjukkan perbedaan nyata pada variabel pertumbuhan panjang total , dengan angka tertinggi 0,36 cm pada perlakuan C (Batu roster) dan terendah dengan angka 0,23 cm pada perlakuan D (Botol plastik). Sedangkan variabel bobot mutlak, laju pertumbuhan harian, dan kelangsungan hidup tidak memperlihatkan perbedaan nyata pada semua perlakuan